Author name cluster

Arunkumar Byravan

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

10 papers

2 author rows

ICML Conference 2025 Conference Paper

Learning the RoPEs: Better 2D and 3D Position Encodings with STRING

Connor Schenck
Isaac Reid
Mithun George Jacob
Alex Bewley
Joshua Ainslie
David Rendleman
Deepali Jain
Mohit Sharma 0001

We introduce $\textbf{STRING}$: Separable Translationally Invariant Position Encodings. STRING extends Rotary Position Encodings, a recently proposed and widely used algorithm in large language models, via a unifying theoretical framework. Importantly, STRING still provides $\textbf{exact}$ translation invariance, including token coordinates of arbitrary dimensionality, whilst maintaining a low computational footprint. These properties are especially important in robotics, where efficient 3D token representation is key. We integrate STRING into Vision Transformers with RGB(-D) inputs (color plus optional depth), showing substantial gains, e. g. in open-vocabulary object detection and for robotics controllers. We complement our experiments with a rigorous mathematical analysis, proving the universality of our methods. Videos of STRING-based robotics controllers can be found here: https: //sites. google. com/view/string-robotics.

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ICRA Conference 2023 Conference Paper

NeRF2Real: Sim2real Transfer of Vision-guided Bipedal Motion Skills using Neural Radiance Fields

Arunkumar Byravan
Jan Humplik
Leonard Hasenclever
Arthur Brussee
Francesco Nori
Tuomas Haarnoja
Ben Moran
Steven Bohez

We present a system for applying sim2real approaches to “in the wild” scenes with realistic visuals, and to policies which rely on active perception using RGB cameras. Given a short video of a static scene collected using a generic phone, we learn the scene's contact geometry and a function for novel view synthesis using a Neural Radiance Field (NeRF). We augment the NeRF rendering of the static scene by overlaying the rendering of other dynamic objects (e. g. the robot's own body, a ball). A simulation is then created using the rendering engine in a physics simulator which computes contact dynamics from the static scene geometry (estimated from the NeRF vol-ume density) and the dynamic objects' geometry and physical properties (assumed known). We demonstrate that we can use this simulation to learn vision-based whole body navigation and ball pushing policies for a 20 degree-of-freedom humanoid robot with an actuated head-mounted RGB camera, and we successfully transfer these policies to a real robot.

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ICLR Conference 2022 Conference Paper

Evaluating Model-Based Planning and Planner Amortization for Continuous Control

Arunkumar Byravan
Leonard Hasenclever
Piotr Trochim
Mehdi Mirza
Alessandro Davide Ialongo
Yuval Tassa
Jost Tobias Springenberg
Abbas Abdolmaleki

There is a widespread intuition that model-based control methods should be able to surpass the data efficiency of model-free approaches. In this paper we attempt to evaluate this intuition on various challenging locomotion tasks. We take a hybrid approach, combining model predictive control (MPC) with a learned model and model-free policy learning; the learned policy serves as a proposal for MPC. We show that MPC with learned proposals and models (trained on the fly or transferred from related tasks) can significantly improve performance and data efficiency with respect to model-free methods. However, we find that well-tuned model-free agents are strong baselines even for high DoF control problems. Finally, we show that it is possible to distil a model-based planner into a policy that amortizes the planning computation without any loss of performance.

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ICRA Conference 2021 Conference Paper

Representation Matters: Improving Perception and Exploration for Robotics

Markus Wulfmeier
Arunkumar Byravan
Tim Hertweck
Irina Higgins
Ankush Gupta
Tejas Kulkarni
Malcolm Reynolds
Denis Teplyashin

Projecting high-dimensional environment observations into lower-dimensional structured representations can considerably improve data-efficiency for reinforcement learning in domains with limited data such as robotics. Can a single generally useful representation be found? In order to answer this question, it is important to understand how the representation will be used by the agent and what properties such a good representation should have. In this paper we systematically evaluate a number of common learnt and hand-engineered representations in the context of three robotics tasks: lifting, stacking and pushing of 3D blocks. The representations are evaluated in two use-cases: as input to the agent, or as a source of auxiliary tasks. Furthermore, the value of each representation is evaluated in terms of three properties: dimensionality, observability and disentanglement. We can significantly improve performance in both use-cases and demonstrate that some representations can perform commensurate to simulator states as agent inputs. Finally, our results challenge common intuitions by demonstrating that: 1) dimensionality strongly matters for task generation, but is negligible for inputs, 2) observability of task-relevant aspects mostly affects the input representation use-case, and 3) disentanglement leads to better auxiliary tasks, but has only limited benefits for input representations. This work serves as a step towards a more systematic understanding of what makes a good representation for control in robotics, enabling practitioners to make more informed choices for developing new learned or hand-engineered representations.

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ICRA Conference 2019 Conference Paper

Prospection: Interpretable plans from language by predicting the future

Chris Paxton 0001
Yonatan Bisk
Jesse Thomason
Arunkumar Byravan
Dieter Fox

High-level human instructions often correspond to behaviors with multiple implicit steps. In order for robots to be useful in the real world, they must be able to to reason over both motions and intermediate goals implied by human instructions. In this work, we propose a framework for learning representations that convert from a natural-language command to a sequence of intermediate goals for execution on a robot. A key feature of this framework is prospection, training an agent not just to correctly execute the prescribed command, but to predict a horizon of consequences of an action before taking it. We demonstrate the fidelity of plans generated by our framework when interpreting real, crowd-sourced natural language commands for a robot in simulated scenes.

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ICRA Conference 2018 Conference Paper

SE3-Pose-Nets: Structured Deep Dynamics Models for Visuomotor Control

Arunkumar Byravan
Felix Leeb
Franziska Meier
Dieter Fox

In this work, we present an approach to deep visuomotor control using structured deep dynamics models. Our model, a variant of SE3-Nets, learns a low-dimensional pose embedding for visuomotor control via an encoder-decoder structure. Unlike prior work, our model is structured: given an input scene, our network explicitly learns to segment salient parts and predict their pose embedding and motion, modeled as a change in the pose due to the applied actions. We train our model using a pair of point clouds separated by an action and show that given supervision only through point-wise data associations between the frames our network is able to learn a meaningful segmentation of the scene along with consistent poses. We further show that our model can be used for closed-loop control directly in the learned low-dimensional pose space, where the actions are computed by minimizing pose error using gradient-based methods, similar to traditional model-based control. We present results on controlling a Baxter robot from raw depth data in simulation and RGBD data in the real world and compare against two baseline deep networks. We also test the robustness and generalization performance of our controller under changes in camera pose, lighting, occlusion, and motion. Our method is robust, runs in real-time, achieves good prediction of scene dynamics, and outperforms baselines on multiple control runs. Video results can be found at: https://rse-lab.cs.washington.edu/se3-structured-deep-ctrl/.

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ICRA Conference 2017 Conference Paper

SE3-nets: Learning rigid body motion using deep neural networks

Arunkumar Byravan
Dieter Fox

We introduce SE3-Nets which are deep neural networks designed to model and learn rigid body motion from raw point cloud data. Based only on sequences of depth images along with action vectors and point wise data associations, SE3-Nets learn to segment effected object parts and predict their motion resulting from the applied force. Rather than learning point wise flow vectors, SE3-Nets predict SE(3) transformations for different parts of the scene. Using simulated depth data of a table top scene and a robot manipulator, we show that the structure underlying SE3-Nets enables them to generate a far more consistent prediction of object motion than traditional flow based networks. Additional experiments with a depth camera observing a Baxter robot pushing objects on a table show that SE3-Nets also work well on real data.

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IJCAI Conference 2015 Conference Paper

Graph-Based Inverse Optimal Control for Robot Manipulation

Arunkumar Byravan
Mathew Monfort
Brian Ziebart
Byron Boots
Dieter Fox

Inverse optimal control (IOC) is a powerful approach for learning robotic controllers from demonstration that estimates a cost function which rationalizes demonstrated control trajectories. Unfortunately, its applicability is difficult in settings where optimal control can only be solved approximately. While local IOC approaches have been shown to successfully learn cost functions in such settings, they rely on the availability of good reference trajectories, which might not be available at test time. We address the problem of using IOC in these computationally challenging control tasks by using a graph-based discretization of the trajectory space. Our approach projects continuous demonstrations onto this discrete graph, where a cost function can be tractably learned via IOC. Discrete control trajectories from the graph are then projected back to the original space and locally optimized using the learned cost function. We demonstrate the effectiveness of the approach with experiments conducted on two 7-degree of freedom robotic arms.

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ICRA Conference 2014 Conference Paper

Learning predictive models of a depth camera & manipulator from raw execution traces

Byron Boots
Arunkumar Byravan
Dieter Fox

In this paper, we attack the problem of learning a predictive model of a depth camera and manipulator directly from raw execution traces. While the problem of learning manipulator models from visual and proprioceptive data has been addressed before, existing techniques often rely on assumptions about the structure of the robot or tracked features in observation space. We make no such assumptions. Instead, we formulate the problem as that of learning a high-dimensional controlled stochastic process. We leverage recent work on nonparametric predictive state representations to learn a generative model of the depth camera and robotic arm from sequences of uninterpreted actions and observations. We perform several experiments in which we demonstrate that our learned model can accurately predict future depth camera observations in response to sequences of motor commands.

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ICRA Conference 2014 Conference Paper

Space-time functional gradient optimization for motion planning

Arunkumar Byravan
Byron Boots
Siddhartha S. Srinivasa
Dieter Fox

Functional gradient algorithms (e. g. CHOMP) have recently shown great promise for producing locally optimal motion for complex many degree-of-freedom robots. A key limitation of such algorithms is the difficulty in incorporating constraints and cost functions that explicitly depend on time. We present T-CHOMP, a functional gradient algorithm that overcomes this limitation by directly optimizing in space-time. We outline a framework for joint space-time optimization, derive an efficient trajectory-wide update for maintaining time monotonicity, and demonstrate the significance of T-CHOMP over CHOMP in several scenarios. By manipulating time, T-CHOMP produces lower-cost trajectories leading to behavior that is meaningfully different from CHOMP.

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