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Ioannis Havoutis

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35 papers
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35

TMLR Journal 2026 Journal Article

MetaSym: A Symplectic Meta-learning Framework for Physical Intelligence

  • Pranav Vaidhyanathan
  • Aristotelis Papatheodorou
  • Mark T. Mitchison
  • Natalia Ares
  • Ioannis Havoutis

Scalable and generalizable physics-aware deep learning has long been considered a significant challenge with various applications across diverse domains ranging from robotics to molecular dynamics. Central to almost all physical systems are symplectic forms, the geometric backbone that underpins fundamental invariants like energy and momentum. In this work, we introduce a novel deep learning framework, MetaSym. In particular, MetaSym combines a strong symplectic inductive bias obtained from a symplectic encoder, and an autoregressive decoder with meta-attention. This principled design ensures that core physical invariants remain intact, while allowing flexible, data efficient adaptation to system heterogeneities. We benchmark MetaSym with highly varied and realistic datasets, such as a high-dimensional spring-mesh system Otness et al. (2021), an open quantum system with dissipation and measurement backaction, and robotics-inspired quadrotor dynamics. Crucially, we fine-tune and deploy MetaSym on real-world quadrotor data, demonstrating robustness to sensor noise and real-world uncertainty. Across all tasks, MetaSym achieves superior few-shot adaptation and outperforms larger state-of-the-art (SOTA) models.

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IROS Conference 2025 Conference Paper

Adaptive Manipulation using Behavior Trees

  • Jacques Cloete
  • Wolfgang Merkt
  • Ioannis Havoutis

Many manipulation tasks pose a challenge since they depend on non-visual environmental information that can only be determined after sustained physical interaction has already begun. This is particularly relevant for effort-sensitive, dynamics-dependent tasks such as tightening a valve. To perform these tasks safely and reliably, robots must be able to quickly adapt in response to unexpected changes during task execution, and should also learn from past experience to better inform future decisions. Humans can intuitively respond and adapt their manipulation strategy to suit such problems, but representing and implementing such behaviors for robots remains a challenge. In this work we show how this can be achieved within the framework of behavior trees. We present the adaptive behavior tree, a scalable and generalizable behavior tree design that enables a robot to quickly adapt to and learn from both visual and non-visual observations during task execution, preempting task failure or switching to a different manipulation strategy. The adaptive behavior tree selects the manipulation strategy that is predicted to optimize task performance, and learns from past experience to improve these predictions for future attempts. We test our approach on a variety of tasks commonly found in industry; the adaptive behavior tree demonstrates safety, robustness (100% success rate) and efficiency in task completion (up to 36% task speedup from the baseline).

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ICRA Conference 2025 Conference Paper

Offline Adaptation of Quadruped Locomotion Using Diffusion Models

  • Reece O'Mahoney
  • Alexander L. Mitchell
  • Wanming Yu
  • Ingmar Posner
  • Ioannis Havoutis

We present a diffusion-based approach to quadrupedal locomotion that simultaneously addresses the limitations of learning and interpolating between multiple skills (modes) and of offline adapting to new locomotion behaviours after training. This is the first framework to apply classifier-free guided diffusion to quadruped locomotion and demonstrate its efficacy by extracting goal-conditioned behaviour from an originally unlabelled dataset. We show that these capabilities are compatible with a multi-skill policy and can be applied with little modification and minimal compute overhead, i. e. , running entirely on the robot's onboard CPU. We verify the validity of our approach with hardware experiments on the ANYmal quadruped platform.

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IROS Conference 2024 Conference Paper

Learning Generalizable Manipulation Policy with Adapter-Based Parameter Fine-Tuning

  • Kai Lu 0003
  • Kim Tien Ly
  • William Hebberd
  • Kaichen Zhou
  • Ioannis Havoutis
  • Andrew Markham

This study investigates the use of adapters in reinforcement learning for robotic skill generalization across multiple robots and tasks. Traditional methods are typically reliant on robot-specific retraining and face challenges such as efficiency and adaptability, particularly when scaling to robots with varying kinematics. We propose an alternative approach where a disembodied (virtual) hand manipulator learns a task (i. e. , an abstract skill) and then transfers it to various robots with different kinematic constraints without retraining the entire model (i. e. , the concrete, physical implementation of the skill). Whilst adapters are commonly used in other domains with strong supervision available, we show how weaker feedback from robotic control can be used to optimize task execution by preserving the abstract skill dynamics whilst adapting to new robotic domains. We demonstrate the effectiveness of our method with experiments conducted in the SAPIEN ManiSkill environment, showing improvements in generalization and task success rates. All code, data, and additional videos are at this GitHub link: https://kl-research.github.io/genrob.

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IROS Conference 2024 Conference Paper

Momentum-Aware Trajectory Optimisation using Full-Centroidal Dynamics and Implicit Inverse Kinematics

  • Aristotelis Papatheodorou
  • Wolfgang Merkt
  • Alexander L. Mitchell
  • Ioannis Havoutis

The current state-of-the-art gradient-based optimisation frameworks are able to produce impressive dynamic manoeuvres such as linear and rotational jumps. However, these methods, which optimise over the full rigid-body dynamics of the robot, often require precise foothold locations apriori, while real-time performance is not guaranteed without elaborate regularisation and tuning of the cost function. In contrast, we investigate the advantages of a task-space optimisation framework, with special focus on acrobatic motions. Our proposed formulation exploits the system's high-order nonlinearities, such as the nonholonomy of the angular momentum, in order to produce feasible, high-acceleration manoeuvres. By leveraging the full-centroidal dynamics of the quadruped ANYmal C and directly optimising its footholds and contact forces, the framework is capable of producing efficient motion plans with low computational overhead. Finally, we deploy our proposed framework on the ANYmal C platform, and demonstrate its true capabilities through real-world experiments, with the successful execution of high-acceleration motions, such as linear and rotational jumps. Extensive analysis of these shows that the robot's dynamics can be exploited to surpass its hardware limitations of having a high mass and low-torque limits.

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ICRA Conference 2024 Conference Paper

R-LGP: A Reachability-guided Logic-geometric Programming Framework for Optimal Task and Motion Planning on Mobile Manipulators

  • Kim Tien Ly
  • Valeriy Semenov
  • Mattia Risiglione
  • Wolfgang Merkt
  • Ioannis Havoutis

This paper presents an optimization-based solution to task and motion planning (TAMP) on mobile manipulators. Logic-geometric programming (LGP) has shown promising capabilities for optimally dealing with hybrid TAMP problems that involve abstract and geometric constraints. However, LGP does not scale well to high-dimensional systems (e. g. mobile manipulators) and can suffer from obstacle avoidance issues due to local minima. In this work, we extend LGP with a sampling-based reachability graph to enable solving optimal TAMP on high-DoF mobile manipulators. The proposed reachability graph can incorporate environmental information (obstacles) to provide the planner with sufficient geometric constraints. This reachability-aware heuristic efficiently prunes infeasible sequences of actions in the continuous domain, hence, it reduces replanning by securing feasibility at the final full path trajectory optimization. Our framework proves to be time-efficient in computing optimal and collision-free solutions, while outperforming the current state of the art on metrics of success rate, planning time, path length and number of steps. We validate our framework on the physical Toyota HSR robot and report comparisons on a series of mobile manipulation tasks of increasing difficulty. Videos of the experiments are available here.

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ICRA Conference 2023 Conference Paper

Learning Low-Frequency Motion Control for Robust and Dynamic Robot Locomotion

  • Siddhant Gangapurwala
  • Luigi Campanaro
  • Ioannis Havoutis

Robotic locomotion is often approached with the goal of maximizing robustness and reactivity by increasing motion control frequency. We challenge this intuitive notion by demonstrating robust and dynamic locomotion with a learned motion controller executing at as low as 8 Hz on a real ANYmal C quadruped. The robot is able to robustly and repeatably achieve a high heading velocity of 1. 5 ms-1, traverse uneven terrain, and resist unexpected external perturbations. We further present a comparative analysis of deep reinforcement learning (RL) based motion control policies trained and executed at frequencies ranging from 5 Hz to 200 Hz. We show that low-frequency policies are less sensitive to actuation latencies and variations in system dynamics. This is to the extent that a successful sim- to-real transfer can be performed even without any dynamics randomization or actuation modeling. We support this claim through a set of rigorous empirical evaluations. Moreover, to assist reproducibility, we provide the training and deployment code along with an extended analysis at https://ori-drs.github.io/lfmc/.

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ICRA Conference 2023 Conference Paper

Leveraging Scene Embeddings for Gradient-Based Motion Planning in Latent Space

  • Jun Yamada
  • Chia-Man Hung
  • Jack Collins
  • Ioannis Havoutis
  • Ingmar Posner

Motion planning framed as optimisation in structured latent spaces has recently emerged as competitive with traditional methods in terms of planning success while significantly outperforming them in terms of computational speed. However, the real-world applicability of recent work in this domain remains limited by the need to express obstacle information directly in state-space, involving simple geometric primitives. In this work we address this challenge by leveraging learned scene embeddings together with a generative model of the robot manipulator to drive the optimisation process. In addition, we introduce an approach for efficient collision checking which directly regularises the optimisation undertaken for planning. Using simulated as well as real-world experiments, we demonstrate that our approach, AMP-LS, is able to successfully plan in novel, complex scenes while outperforming traditional planning baselines in terms of computation speed by an order of magnitude. We show that the resulting system is fast enough to enable closed-loop planning in real-world dynamic scenes.

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ICRA Conference 2022 Conference Paper

Next Steps: Learning a Disentangled Gait Representation for Versatile Quadruped Locomotion

  • Alexander L. Mitchell
  • Wolfgang Merkt
  • Mathieu Geisert
  • Siddhant Gangapurwala
  • Martin Engelcke
  • Oiwi Parker Jones
  • Ioannis Havoutis
  • Ingmar Posner

Quadruped locomotion is rapidly maturing to a degree where robots now routinely traverse a variety of unstructured terrains. However, while gaits can be varied typically by selecting from a range of pre-computed styles, current planners are unable to vary key gait parameters continuously while the robot is in motion. The synthesis, on-the-fly, of gaits with unexpected operational characteristics or even the blending of dynamic manoeuvres lies beyond the capabilities of the current state-of-the-art. In this work we address this limitation by learning a latent space capturing the key stance phases of a particular gait, via a generative model trained on a single trot style. This encourages disentanglement such that application of a drive signal to a single dimension of the latent state induces holistic plans synthesising a continuous variety of trot styles. In fact properties of this drive signal map directly to gait parameters such as cadence, footstep height and full stance duration. The use of a generative model facilitates the detection and mitigation of disturbances to provide a versatile and robust planning framework. We evaluate our approach on a real ANYmal quadruped robot and demonstrate that our method achieves a continuous blend of dynamic trot styles whilst being robust and reactive to external perturbations.

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ICRA Conference 2022 Conference Paper

Semantically Grounded Object Matching for Robust Robotic Scene Rearrangement

  • Walter Goodwin
  • Sagar Vaze
  • Ioannis Havoutis
  • Ingmar Posner

Object rearrangement has recently emerged as a key competency in robot manipulation, with practical solutions generally involving object detection, recognition, grasping and high-level planning. Goal-images describing a desired scene configuration are a promising and increasingly used mode of instruction. A key outstanding challenge is the accurate inference of matches between objects in front of a robot, and those seen in a provided goal image, where recent works have struggled in the absence of object-specific training data. In this work, we explore the deterioration of existing methods' ability to infer matches between objects as the visual shift between observed and goal scenes increases. We find that a fundamental limitation of the current setting is that source and target images must contain the same instance of every object, which restricts practical deployment. We present a novel approach to object matching that uses a large pre-trained vision-language model to match objects in a cross-instance setting by leveraging semantics together with visual features as a more robust, and much more general, measure of similarity. We demonstrate that this provides considerably improved matching performance in cross-instance settings, and can be used to guide multi-object rearrangement with a robot manipulator from an image that shares no object instances with the robot's scene. Our code is available at https://github.com/applied-ai-lab/object_matching.

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ICRA Conference 2021 Conference Paper

A Passive Navigation Planning Algorithm for Collision-free Control of Mobile Robots

  • Carlo Tiseo
  • Vladimir Ivan
  • Wolfgang Merkt
  • Ioannis Havoutis
  • Michael N. Mistry
  • Sethu Vijayakumar

Path planning and collision avoidance are challenging in complex and highly variable environments due to the limited horizon of events. In literature, there are multiple model- and learning-based approaches that require significant computational resources to be effectively deployed and they may have limited generality. We propose a planning algorithm based on a globally stable passive controller that can plan smooth trajectories using limited computational resources in challenging environmental conditions. The architecture combines the recently proposed fractal impedance controller with elastic bands and regions of finite time invariance. As the method is based on an impedance controller, it can also be used directly as a force/torque controller. We validated our method in simulation to analyse the ability of interactive navigation in challenging concave domains via the issuing of via-points, and its robustness to low bandwidth feedback. A swarm simulation using 11 agents validated the scalability of the proposed method. We have performed hardware experiments on a holonomic wheeled platform validating smoothness and robustness of interaction with dynamic agents (i. e. , humans and robots). The computational complexity of the proposed local planner enables deployment with low-power micro-controllers lowering the energy consumption compared to other methods that rely upon numeric optimisation.

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ICRA Conference 2021 Conference Paper

Introspective Visuomotor Control: Exploiting Uncertainty in Deep Visuomotor Control for Failure Recovery

  • Chia-Man Hung
  • Li Sun 0005
  • Yizhe Wu
  • Ioannis Havoutis
  • Ingmar Posner

End-to-end visuomotor control is emerging as a compelling solution for robot manipulation tasks. However, imitation learning-based visuomotor control approaches tend to suffer from a common limitation, lacking the ability to recover from an out-of-distribution state caused by compounding errors. In this paper, instead of using tactile feedback or explicitly detecting the failure through vision, we investigate using the uncertainty of a policy neural network. We propose a novel uncertainty-based approach to detect and recover from failure cases. Our hypothesis is that policy uncertainties can implicitly indicate the potential failures in the visuomotor control task and that robot states with minimum uncertainty are more likely to lead to task success. To recover from high uncertainty cases, the robot monitors its uncertainty along a trajectory and explores possible actions in the state-action space to bring itself to a more certain state. Our experiments verify this hypothesis and show a significant improvement on task success rate: 12% in pushing, 15% in pick-and-reach and 22% in pick-and-place.

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ICRA Conference 2021 Conference Paper

Inverse Dynamics vs. Forward Dynamics in Direct Transcription Formulations for Trajectory Optimization

  • Henrique Ferrolho
  • Vladimir Ivan
  • Wolfgang Merkt
  • Ioannis Havoutis
  • Sethu Vijayakumar

Benchmarks of state-of-the-art rigid-body dynamics libraries report better performance solving the inverse dynamics problem than the forward alternative. Those benchmarks encouraged us to question whether that computational advantage would translate to direct transcription, where calculating rigid-body dynamics and their derivatives accounts for a significant share of computation time. In this work, we implement an optimization framework where both approaches for enforcing the system dynamics are available. We evaluate the performance of each approach for systems of varying complexity, for domains with rigid contacts. Our tests reveal that formulations using inverse dynamics converge faster, require less iterations, and are more robust to coarse problem discretization. These results indicate that inverse dynamics should be preferred to enforce the nonlinear system dynamics in simultaneous methods, such as direct transcription.

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ICAPS Conference 2021 Conference Paper

Predicted Composite Signed-Distance Fields for Real-Time Motion Planning in Dynamic Environments

  • Mark Nicholas Finean
  • Wolfgang Merkt
  • Ioannis Havoutis

We present a novel framework for motion planning in dynamic environments that accounts for the predicted trajectories of moving objects. We explore the use of composite signed-distance fields in motion planning and detail how they can be used to generate signed-distance fields (SDFs) in real-time to incorporate predicted obstacle motions; to achieve this, we introduce the concept of predicted signed-distance fields. We benchmark our approach of using composite SDFs against performing exact SDF calculations on the workspace occupancy grid. Our proposed technique generates predictions substantially faster and typically exhibits an 81-97% reduction in time for subsequent predictions. We integrate our framework with GPMP2 to demonstrate a full implementation of our approach in real-time, enabling a 7-DoF Panda manipulator to smoothly avoid a moving obstacle in simulation and hardware experiments.

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IROS Conference 2021 Conference Paper

Rapid Convex Optimization of Centroidal Dynamics using Block Coordinate Descent

  • Paarth Shah
  • Avadesh Meduri
  • Wolfgang Merkt
  • Majid Khadiv
  • Ioannis Havoutis
  • Ludovic Righetti

In this paper we explore the use of block coordinate descent (BCD) to optimize the centroidal momentum dynamics for dynamically consistent multi-contact behaviors. The centroidal dynamics have recently received a large amount of attention in order to create physically realizable motions for robots with hands and feet while being computationally more tractable than full rigid body dynamics models. Our contribution lies in exploiting the structure of the dynamics in order to simplify the original non-convex problem into two convex subproblems. We iterate between these two subproblems for a set number of iterations or until a consensus is reached. We explore the properties of the proposed optimization method for the centroidal dynamics and verify in simulation that motions generated by our approach can be tracked by the quadruped Solo12. In addition, we compare our method to a recently proposed convexification using a sequence of convex relaxations as well as a more standard interior point method used in the off-the-shelf solver IPOPT to show that our approach finds similar, if not better, trajectories (in terms of cost), and is more than four times faster than both approaches. Finally, compared to previous approaches, we note its practicality due to the convex nature of each subproblem which allows our method to be used with any off-the-shelf quadratic programming solver.

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IROS Conference 2021 Conference Paper

Rapid Stability Margin Estimation for Contact-Rich Locomotion

  • Romeo Orsolino
  • Siddhant Gangapurwala
  • Oliwier Melon
  • Mathieu Geisert
  • Ioannis Havoutis
  • Maurice F. Fallon

The efficient evaluation the dynamic stability of legged robots on non-coplanar terrains is important when developing motion planning and control policies. The inference time of this measure has a strong influence on how fast a robot can react to unexpected events, plan its future footsteps or its body trajectory. Existing approaches suitable for real-time decision making are either limited to flat ground or to quasi-static locomotion. Furthermore, joint-space feasibility constraints are usually not considered in receding-horizon planning as their high dimensionality prohibits this. In this paper we propose the usage of a stability criterion for dynamic locomotion on rough terrain based on the Feasible Region (FR) and the Instantaneous Capture Point (ICP) and we leverage a Neural Network (NN) to quickly estimate it. We show that our network achieves satisfactory accuracy with respect to its analytical counterpart with a speed up of three orders-of-magnitude. It also enables the evaluation of the stability margin's gradient. We demonstrate this learned stability margin in two diverse applications - Reinforcement Learning (RL) and nonlinear Trajectory Optimization (TO) for legged robots. We demonstrate on a full-sized quadruped robot that the network enables the computation of physically-realizable Center of Mass (CoM) trajectories and foothold locations satisfying friction constraints and joint-torque limits in a receding-horizon fashion and on non-coplanar terrains.

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ICRA Conference 2021 Conference Paper

Real-Time Trajectory Adaptation for Quadrupedal Locomotion using Deep Reinforcement Learning

  • Siddhant Gangapurwala
  • Mathieu Geisert
  • Romeo Orsolino
  • Maurice F. Fallon
  • Ioannis Havoutis

We present a control architecture for real-time adaptation and tracking of trajectories generated using a terrain-aware trajectory optimization solver. This approach enables us to circumvent the computationally exhaustive task of online trajectory optimization, and further introduces a control solution robust to systems modeled with approximated dynamics. We train a policy using deep reinforcement learning (RL) to introduce additive deviations to a reference trajectory in order to generate a feedback-based trajectory tracking system for a quadrupedal robot. We train this policy across a multitude of simulated terrains and ensure its generality by introducing training methods that avoid overfitting and convergence towards local optima. Additionally, in order to capture terrain information, we include a latent representation of the height maps in the observation space of the RL environment as a form of exteroceptive feedback. We test the performance of our trained policy by tracking the corrected set points using a model-based whole-body controller and compare it with the tracking behavior obtained without the corrective feedback in several simulation environments, and show that introducing the corrective feedback results in increase of the success rate from 72. 7% to 92. 4% for tracking precomputed dynamic long horizon trajectories on flat terrain and from 47. 5% to 80. 3% on a complex modular uneven terrain. We also show successful transfer of our training approach to the real physical system and further present cogent arguments in support of our framework.

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ICRA Conference 2021 Conference Paper

Receding-Horizon Perceptive Trajectory Optimization for Dynamic Legged Locomotion with Learned Initialization

  • Oliwier Melon
  • Romeo Orsolino
  • David Surovik
  • Mathieu Geisert
  • Ioannis Havoutis
  • Maurice F. Fallon

To dynamically traverse challenging terrain, legged robots need to continually perceive and reason about upcoming features, adjust the locations and timings of future footfalls and leverage momentum strategically. We present a pipeline that enables flexibly-parametrized trajectories for perceptive and dynamic quadruped locomotion to be optimized in an online, receding-horizon manner. The initial guess passed to the optimizer affects the computation needed to achieve convergence and the quality of the solution. We consider two methods for generating good guesses. The first is a heuristic initializer which provides a simple guess and requires significant optimization but is nonetheless suitable for adaptation to upcoming terrain. We demonstrate experiments using the ANYmal C quadruped, with fully onboard sensing and computation, to cross obstacles at moderate speeds using this technique. Our second approach uses latent-mode trajectory regression (LMTR) to imitate expert data—while avoiding invalid interpolations between distinct behaviors—such that minimal optimization is needed. This enables high-speed motions that make more expansive use of the robot’s capabilities. We demonstrate it on flat ground with the real robot and provide numerical trials that progress toward deployment on terrain. These results illustrate a paradigm for advancing beyond short-horizon dynamic reactions, toward the type of intuitive and adaptive locomotion planning exhibited by animals and humans.

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IROS Conference 2021 Conference Paper

Simultaneous Scene Reconstruction and Whole-Body Motion Planning for Safe Operation in Dynamic Environments

  • Mark Nicholas Finean
  • Wolfgang Merkt
  • Ioannis Havoutis

Recent work has demonstrated real-time mapping and reconstruction from dense perception, while motion planning based on distance fields has been shown to achieve fast, collision-free motion synthesis with good convergence properties. However, demonstration of a fully integrated system that can safely re-plan in unknown environments, in the presence of static and dynamic obstacles, has remained an open challenge. In this work, we first study the impact that signed and unsigned distance fields have on optimisation convergence, and the resultant error cost in trajectory optimisation problems in 2D path planning, arm manipulator motion planning, and whole-body loco-manipulation planning. We further analyse the performance of three state-of-the-art approaches to generating distance fields (Voxblox, Fiesta, and GPU-Voxels) for use in realtime environment reconstruction. Finally, we use our findings to construct a practical hybrid mapping and motion planning system which uses GPU-Voxels and GPMP2 to perform receding- horizon whole-body motion planning that can smoothly avoid moving obstacles in 3D space using live sensor data. Our results are validated in simulation and on a real-world Toyota Human Support Robot (HSR).

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ICRA Conference 2021 Conference Paper

Sparsity-Inducing Optimal Control via Differential Dynamic Programming

  • Traiko Dinev
  • Wolfgang Merkt
  • Vladimir Ivan
  • Ioannis Havoutis
  • Sethu Vijayakumar

Optimal control is a popular approach to synthesize highly dynamic motion. Commonly, L 2 regularization is used on the control inputs in order to minimize energy used and to ensure smoothness of the control inputs. However, for some systems, such as satellites, the control needs to be applied in sparse bursts due to how the propulsion system operates. In this paper, we study approaches to induce sparsity in optimal control solutions—namely via smooth L 1 and Huber regularization penalties. We apply these loss terms to state-of-the-art Differential Dynamic Programming (DDP)-based solvers to create a family of sparsity-inducing optimal control methods. We analyze and compare the effect of the different losses on inducing sparsity, their numerical conditioning, their impact on convergence, and discuss hyperparameter settings. We demonstrate our method in simulation and hardware experiments on canonical dynamics systems, control of satellites, and the NASA Valkyrie humanoid robot. We provide an implementation of our method and all examples for reproducibility on GitHub.

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IROS Conference 2020 Conference Paper

First Steps: Latent-Space Control with Semantic Constraints for Quadruped Locomotion

  • Alexander L. Mitchell
  • Martin Engelcke
  • Oiwi Parker Jones
  • David Surovik
  • Siddhant Gangapurwala
  • Oliwier Melon
  • Ioannis Havoutis
  • Ingmar Posner

Traditional approaches to quadruped control frequently employ simplified, hand-derived models. This significantly reduces the capability of the robot since its effective kinematic range is curtailed. In addition, kinodynamic constraints are often non-differentiable and difficult to implement in an optimisation approach. In this work, these challenges are addressed by framing quadruped control as optimisation in a structured latent space. A deep generative model captures a statistical representation of feasible joint configurations, whilst complex dynamic and terminal constraints are expressed via high-level, semantic indicators and represented by learned classifiers operating upon the latent space. As a consequence, complex constraints are rendered differentiable and evaluated an order of magnitude faster than analytical approaches. We validate the feasibility of locomotion trajectories optimised using our approach both in simulation and on a real-world ANY-mal quadruped. Our results demonstrate that this approach is capable of generating smooth and realisable trajectories. To the best of our knowledge, this is the first time latent space control has been successfully applied to a complex, real robot platform.

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ICAPS Conference 2020 Conference Paper

Learning Sequences of Approximations for Hierarchical Motion Planning

  • Martim Brandão
  • Ioannis Havoutis

The process of designing hierarchical motion planners typically involves problem-specific intuition and implementations. This process is sub-optimal both in terms of solution space (amount of possibilities for search-space approximations, choice of planner parameters, etc) and amount of human labour. In this paper we show that the design of hierarchical motion planners does not have to be manual. We present a method for parameterizing and then optimizing sequences of problem approximations used in hierarchical motion planning. We define these as a specific kind of graph with intermediate state-spaces and solutions as nodes, and costs and planner parameters as edge properties. These properties become a continuous optimization variable that changes the sequence and parameters of sub-planners in the hierarchy. Using Pareto-front estimation, our method automatically discovers multiple designs of optimal computation-time/motion-cost trade-offs. We evaluate the method on a set of legged robot motion planning problems where hand-designed hierarchies are abundant. Our method discovers sequences of problem approximations which achieve similar—though slightly higher—performance than the best human-designed hierarchies. The performance gain significantly increases on new problems, yielding 12x faster computation times and 10% higher success rates.

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ICRA Conference 2020 Conference Paper

Reliable Trajectories for Dynamic Quadrupeds using Analytical Costs and Learned Initializations

  • Oliwier Melon
  • Mathieu Geisert
  • David Surovik
  • Ioannis Havoutis
  • Maurice F. Fallon

Dynamic traversal of uneven terrain is a major objective in the field of legged robotics. The most recent model predictive control approaches for these systems can generate robust dynamic motion of short duration; however, planning over a longer time horizon may be necessary when navigating complex terrain. A recently-developed framework, Trajectory Optimization for Walking Robots (TOWR), computes such plans but does not guarantee their reliability on real platforms, under uncertainty and perturbations. We extend TOWR with analytical costs to generate trajectories that a state-of-the-art whole-body tracking controller can successfully execute. To reduce online computation time, we implement a learning-based scheme for initialization of the nonlinear program based on offline experience. The execution of trajectories as long as 16 footsteps and 5. 5 s over different terrains by a real quadruped demonstrates the effectiveness of the approach on hardware. This work builds toward an online system which can efficiently and robustly replan dynamic trajectories.

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IROS Conference 2019 Conference Paper

Multi-controller multi-objective locomotion planning for legged robots

  • Martim Brandão
  • Maurice F. Fallon
  • Ioannis Havoutis

Different legged robot locomotion controllers offer different advantages; from speed of motion to energy, computational demand, safety and others. In this paper we propose a method for planning locomotion with multiple controllers and sub-planners, explicitly considering the multi-objective nature of the legged locomotion planning problem. The planner first obtains body paths extended with a choice of controller or sub-planner, and then fills the gaps by sub-planning. The method leads to paths with a mix of static and dynamic walking which only plan footsteps where necessary. We show that our approach is faster than pure footstep planning methods both in computation (2x) and mission time (1. 4x), and safer than pure dynamic-walking methods. In addition, we propose two methods for aggregating the multiple objectives in search-based planning and reach desirable trade-offs without weight tuning. We show that they reach desirable Pareto-optimal solutions up to 8x faster than fairly-tuned traditional weighted-sum methods. Our conclusions are drawn from a combination of planning, physics simulation, and real robot experiments.

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IROS Conference 2017 Conference Paper

Learning task-space synergies using Riemannian geometry

  • Martijn J. A. Zeestraten
  • Ioannis Havoutis
  • Sylvain Calinon
  • Darwin G. Caldwell

In the context of robotic control, synergies can form elementary units of behavior. By specifying task-dependent coordination behaviors at a low control level, one can achieve task-specific disturbance rejection. In this work we present an approach to learn the parameters of such low-level controllers by demonstration. We identify a synergy by extracting covariance information from demonstration data. The extracted synergy is used to derive a time-invariant state feedback controller through optimal control. To cope with the non-Euclidean nature of robot poses, we utilize Riemannian geometry, where both estimation of the covariance and the associated controller take into account the geometry of the pose manifold. We demonstrate the efficacy of the approach experimentally in a bimanual manipulation task.

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ICRA Conference 2017 Conference Paper

Supervisory teleoperation with online learning and optimal control

  • Ioannis Havoutis
  • Sylvain Calinon

We present a general approach for online learning and optimal control of manipulation tasks in a supervisory teleoperation context, targeted to underwater remotely operated vehicles (ROVs). We use an online Bayesian nonparametric learning algorithm to build models of manipulation motions as task-parametrized hidden semi-Markov models (TP-HSMM) that capture the spatiotemporal characteristics of demonstrated motions in a probabilistic representation. Motions are then executed autonomously using an optimal controller, namely a model predictive control (MPC) approach in a receding horizon fashion. This way the remote system locally closes a high-frequency control loop that robustly handles noise and dynamically changing environments. Our system automates common and recurring tasks, allowing the operator to focus only on the tasks that genuinely require human intervention. We demonstrate how our solution can be used for a hot-stabbing motion in an underwater teleoperation scenario. We evaluate the performance of the system over multiple trials and compare with a state-of-the-art approach. We report that our approach generalizes well with only a few demonstrations, accurately performs the learned task and adapts online to dynamically changing task conditions.

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ICRA Conference 2017 Conference Paper

Trajectory and foothold optimization using low-dimensional models for rough terrain locomotion

  • Carlos Mastalli
  • Michele Focchi
  • Ioannis Havoutis
  • Andreea Radulescu
  • Sylvain Calinon
  • Jonas Buchli
  • Darwin G. Caldwell
  • Claudio Semini

We present a trajectory optimization framework for legged locomotion on rough terrain. We jointly optimize the center of mass motion and the foothold locations, while considering terrain conditions. We use a terrain costmap to quantify the desirability of a foothold location. We increase the gait's adaptability to the terrain by optimizing the step phase duration and modulating the trunk attitude, resulting in motions with guaranteed stability. We show that the combination of parametric models, stochastic-based exploration and receding horizon planning allows us to handle the many local minima associated with different terrain conditions and walking patterns. This combination delivers robust motion plans without the need for warm-starting. Moreover, we use soft-constraints to allow for increased flexibility when searching in the cost landscape of our problem. We showcase the performance of our trajectory optimization framework on multiple terrain conditions and validate our method in realistic simulation scenarios and experimental trials on a hydraulic, torque controlled quadruped robot.

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ICRA Conference 2017 Conference Paper

Whole-body trajectory optimization for non-periodic dynamic motions on quadrupedal systems

  • Andreea Radulescu
  • Ioannis Havoutis
  • Darwin G. Caldwell
  • Claudio Semini

Autonomous legged robots will be required to handle a wide range of tasks in complex environments. While a lot of research has focused on developing their abilities for periodic locomotion tasks, less effort has been invested in devising generalized strategies for dynamic, non-periodic movements. Motion design approaches are frequently enlisted in the form of teleoperation or predefined heuristics in such scenarios. We employ a realistic simulation of the hydraulically actuated HyQ2Max quadrupedal system for investigations on two distinctive tasks: rearing and posture recovery. We present a whole-body optimization methodology for non-periodic tasks on quadrupedal systems. This approach delivers solutions involving multiple contacts without the need for predefined feet placements. The results obtained show the potential of optimization approaches for motion synthesis in the context of complex tasks.

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ICRA Conference 2016 Conference Paper

Hierarchical planning of dynamic movements without scheduled contact sequences

  • Carlos Mastalli
  • Ioannis Havoutis
  • Michele Focchi
  • Darwin G. Caldwell
  • Claudio Semini

Most animal and human locomotion behaviors for solving complex tasks involve dynamic motions and rich contact interaction. In fact, complex maneuvers need to consider dynamic movement and contact events at the same time. We present a hierarchical trajectory optimization approach for planning dynamic movements with unscheduled contact sequences. We compute whole-body motions that achieve goals that cannot be reached in a kinematic fashion. First, we find a feasible CoM motion according to the centroidal dynamics of the robot. Then, we refine the solution by applying the robot's full-dynamics model, where the feasible CoM trajectory is used as a warm-start point. To accomplish the unscheduled contact behavior, we use complementarity constraints to describe the contact model, i. e. environment geometry and non-sliding active contacts. Both optimization phases are posed as Mathematical Program with Complementarity Constraints (MPCC). Experimental trials demonstrate the performance of our planning approach in a set of challenging tasks.

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ICRA Conference 2015 Conference Paper

Planning and execution of dynamic whole-body locomotion for a hydraulic quadruped on challenging terrain

  • Alexander W. Winkler
  • Carlos Mastalli
  • Ioannis Havoutis
  • Michele Focchi
  • Darwin G. Caldwell
  • Claudio Semini

We present a framework for dynamic quadrupedal locomotion over challenging terrain, where the choice of appropriate footholds is crucial for the success of the behaviour. We build a model of the environment on-line and on-board using an efficient occupancy grid representation. We use Any-time-Repairing A* (ARA*) to search over a tree of possible actions, choose a rough body path and select the locally-best footholds accordingly. We run a n-step lookahead optimization of the body trajectory using a dynamic stability metric, the Zero Moment Point (ZMP), that generates natural dynamic whole-body motions. A combination of floating-base inverse dynamics and virtual model control accurately executes the desired motions on an actively compliant system. Experimental trials show that this framework allows us to traverse terrains at nearly 6 times the speed of our previous work, evaluated over the same set of trials.

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ICRA Conference 2014 Conference Paper

Path planning with force-based foothold adaptation and virtual model control for torque controlled quadruped robots

  • Alexander W. Winkler
  • Ioannis Havoutis
  • Stéphane Bazeille
  • Jesús Ortiz 0001
  • Michele Focchi
  • Rüdiger Dillmann
  • Darwin G. Caldwell
  • Claudio Semini

We present a framework for quadrupedal locomotion over highly challenging terrain where the choice of appropriate footholds is crucial for the success of the behaviour. We use a path planning approach which shares many similarities with the results of the DARPA Learning Locomotion challenge and extend it to allow more flexibility and increased robustness. During execution we incorporate an on-line force-based foothold adaptation mechanism that updates the planned motion according to the perceived state of the environment. This way we exploit the active compliance of our system to smoothly interact with the environment, even when this is inaccurately perceived or dynamically changing, and update the planned path on-the-fly. In tandem we use a virtual model controller that provides the feed-forward torques that allow increased accuracy together with highly compliant behaviour on an otherwise naturally very stiff robotic system. We leverage the full set of benefits that a high performance torque controlled quadruped robot can provide and demonstrate the flexibility and robustness of our approach on a set of experimental trials of increasing difficulty.

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IROS Conference 2013 Conference Paper

Dynamic trot-walking with the hydraulic quadruped robot - HyQ: Analytical trajectory generation and active compliance control

  • Barkan Ugurlu
  • Ioannis Havoutis
  • Claudio Semini
  • Darwin G. Caldwell

This paper presents a trajectory generator and an active compliance control scheme, unified in a framework to synthesize dynamic, feasible and compliant trot-walking locomotion cycles for a stiff-by-nature hydraulically actuated quadruped robot. At the outset, a CoP-based trajectory generator that is constructed using an analytical solution is implemented to obtain feasible and dynamically balanced motion references in a systematic manner. Initial conditions are uniquely determined for symmetrical motion patterns, enforcing that trajectories are seamlessly connected both in position, velocity and acceleration levels, regardless of the given support phase. The active compliance controller, used simultaneously, is responsible for sufficient joint position/force regulation. An admittance block is utilized to compute joint displacements that correspond to joint force errors. In addition to position feedback, these joint displacements are inserted to the position control loop as a secondary feedback term. In doing so, active compliance control is achieved, while the position/force trade-off is modulated via the virtual admittance parameters. Various trot-walking experiments are conducted with the proposed framework using HyQ, a ∼ 75kg hydraulically actuated quadruped robot. We present results of repetitive, continuous, and dynamically equilibrated trot-walking locomotion cycles, both on level surface and uneven surface walking experiments.

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IROS Conference 2013 Conference Paper

Onboard perception-based trotting and crawling with the Hydraulic Quadruped Robot (HyQ)

  • Ioannis Havoutis
  • Jesús Ortiz 0001
  • Stéphane Bazeille
  • Victor Barasuol
  • Claudio Semini
  • Darwin G. Caldwell

This paper presents a framework developed to increase the autonomy and versatility of a large (∼75kg) hydraulically actuated quadrupedal robot. It combines onboard perception with two locomotion strategies, a dynamic trot and a static crawl gait. This way the robot can perceive its environment and arbitrate between the two behaviours according to the situation at hand. All computations are performed on-board and are carried out in two separate computers, one handles the high-level processes while the other is concerned with the low-level hard real-time control. The perception and subsequently the appropriate gait modifications are performed autonomously. We present outdoor experimental trials of the robot trotting over unknown terrain, perceiving a large obstacle, altering its behaviour to the cautious crawl gait and stepping onto the obstacle. This allows the robot to locomote quickly on relatively flat terrain and gives the robot the ability to overcome large irregular obstacles when required.

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IROS Conference 2010 Conference Paper

Constrained geodesic trajectory generation on learnt skill manifolds

  • Ioannis Havoutis
  • Subramanian Ramamoorthy

This paper addresses the problem of compactly encoding a continuous family of trajectories corresponding to a robotic skill, and using this representation for the purpose of constrained trajectory generation in an environment with many (possibly dynamic) obstacles. With a skill manifold that is learnt from data, we show that constraints can be naturally handled within an iterative process of minimizing the total geodesic path length and curvature over the manifold. We demonstrate the utility of this process with two examples. Firstly, a three-link arm whose joint space and corresponding skill manifold can be explicitly visualized. Then, we demonstrate how this procedure can be used to generate constrained walking motions in a humanoid robot.

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ICRA Conference 2010 Conference Paper

Geodesic trajectory generation on learnt skill manifolds

  • Ioannis Havoutis
  • Subramanian Ramamoorthy

Humanoid robots are appealing due to their inherent dexterity. However, these potential benefits may only be realized if the corresponding motion synthesis procedure is suitably flexible. This paper presents a flexible trajectory generation algorithm that utilizes a geometric representation of humanoid skills (e. g. , walking) - in the form of skill manifolds. These manifolds are learnt from demonstration data that may be obtained from off-line optimization algorithms (or a human expert). We demonstrate that this model may be used to produce approximately optimal motion plans as geodesics over the manifold and that this allows us to effectively generalize from a limited training set. We demonstrate the effectiveness of our approach on a simulated 3-link planar arm, and then the more challenging example of a physical 19-DoF humanoid robot. We show that our algorithm produces a close approximation of the much more computationally intensive optimization procedure used to generate the data. This allows us to present experimental results for fast motion planning on a realistic - variable step length, width and height - walking task on a humanoid robot.

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