Author name cluster

Robert Lee

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10 papers

2 author rows

IROS Conference 2024 Conference Paper

DiffusionNOCS: Managing Symmetry and Uncertainty in Sim2Real Multi-Modal Category-level Pose Estimation

Takuya Ikeda
Sergey Zakharov
Tianyi Ko
Muhammad Zubair Irshad
Robert Lee
Katherine Liu
Rares Ambrus
Koichi Nishiwaki

This paper addresses the challenging problem of category-level pose estimation. Current state-of-the-art methods for this task face challenges when dealing with symmetric objects and when attempting to generalize to new environments solely through synthetic data training. In this work, we address these challenges by proposing a probabilistic model that relies on diffusion to estimate dense canonical maps crucial for recovering partial object shapes as well as establishing correspondences essential for pose estimation. Furthermore, we introduce critical components to enhance performance by leveraging the strength of the diffusion models with multi-modal input representations. We demonstrate the effectiveness of our method by testing it on a range of real datasets. Despite being trained solely on our generated synthetic data, our approach achieves state-of-the-art performance and unprecedented generalization qualities, outperforming baselines, even those specifically trained on the target domain. Our code and data for the generalization benchmark can be found at https://woven-planet.github.io/DiffusionNOCS/.

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IROS Conference 2024 Conference Paper

Gravity-aware Grasp Generation with Implicit Grasp Mode Selection for Underactuated Hands

Tianyi Ko
Takuya Ikeda
Thomas Stewart
Robert Lee
Koichi Nishiwaki

Learning-based grasp detectors typically assume a precision grasp, where each finger only has one contact point, and estimate the grasp probability. In this work, we propose a data generation and learning pipeline that can leverage power grasping, which has more contact points with an enveloping configuration and is robust against both positioning error and force disturbance. To train a grasp detector to prioritize power grasping while still keeping precision grasping as the secondary choice, we propose to train the network against the magnitude of disturbance in the gravity direction a grasp can resist (gravity-rejection score) rather than the binary classification of success. We also provide an efficient data generation pipeline for a dataset with gravity-rejection score annotation. Evaluation in both simulation and real-robot clarifies the significant improvement in our approach, especially when the objects are heavy.

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ICRA Conference 2024 Conference Paper

Learning Fabric Manipulation in the Real World with Human Videos

Robert Lee
Jad Abou-Chakra
Fangyi Zhang
Peter Corke

Fabric manipulation is a long-standing challenge in robotics due to the enormous state space and complex dynamics. Learning approaches stand out as promising for this domain as they allow us to learn behaviours directly from data. Most prior methods however rely heavily on simulation, which is still limited by the large sim-to-real gap of deformable objects or rely on large datasets. A promising alternative is to learn fabric manipulation directly from watching humans perform the task. In this work, we explore how demonstrations for fabric manipulation tasks can be collected directly by humans, providing an extremely natural and fast data collection pipeline. Then, using only a handful of such demonstrations, we show how a pick-and-place policy can be learned and deployed on a real robot, without any robot data collection at all. We demonstrate our approach on a fabric smoothing and folding task, showing that our policy can reliably reach folded states from crumpled initial configurations. Code, video and data are available on the project website: https://sites.google.com/view/foldingbyhand

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NeurIPS Conference 2022 Conference Paper

SnAKe: Bayesian Optimization with Pathwise Exploration

Jose Pablo Folch
Shiqiang Zhang
Robert Lee
Behrang Shafei
David Walz
Calvin Tsay
Mark van der Wilk
Ruth Misener

"Bayesian Optimization is a very effective tool for optimizing expensive black-box functions. Inspired by applications developing and characterizing reaction chemistry using droplet microfluidic reactors, we consider a novel setting where the expense of evaluating the function can increase significantly when making large input changes between iterations. We further assume we are working asynchronously, meaning we have to decide on new queries before we finish evaluating previous experiments. This paper investigates the problem and introduces 'Sequential Bayesian Optimization via Adaptive Connecting Samples' (SnAKe), which provides a solution by considering large batches of queries and preemptively building optimization paths that minimize input costs. We investigate some convergence properties and empirically show that the algorithm is able to achieve regret similar to classical Bayesian Optimization algorithms in both the synchronous and asynchronous settings, while reducing the input costs significantly. We show the method is robust to the choice of its single hyper-parameter and provide a parameter-free alternative. "

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NeurIPS Conference 2022 Conference Paper

Tree ensemble kernels for Bayesian optimization with known constraints over mixed-feature spaces

Alexander Thebelt
Calvin Tsay
Robert Lee
Nathan Sudermann-Merx
David Walz
Behrang Shafei
Ruth Misener

Tree ensembles can be well-suited for black-box optimization tasks such as algorithm tuning and neural architecture search, as they achieve good predictive performance with little or no manual tuning, naturally handle discrete feature spaces, and are relatively insensitive to outliers in the training data. Two well-known challenges in using tree ensembles for black-box optimization are (i) effectively quantifying model uncertainty for exploration and (ii) optimizing over the piece-wise constant acquisition function. To address both points simultaneously, we propose using the kernel interpretation of tree ensembles as a Gaussian Process prior to obtain model variance estimates, and we develop a compatible optimization formulation for the acquisition function. The latter further allows us to seamlessly integrate known constraints to improve sampling efficiency by considering domain-knowledge in engineering settings and modeling search space symmetries, e. g. , hierarchical relationships in neural architecture search. Our framework performs as well as state-of-the-art methods for unconstrained black-box optimization over continuous/discrete features and outperforms competing methods for problems combining mixed-variable feature spaces and known input constraints.

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ICRA Conference 2021 Conference Paper

TRANS-AM: Transfer Learning by Aggregating Dynamics Models for Soft Robotic Assembly

Kazutoshi Tanaka
Ryo Yonetani
Masashi Hamaya
Robert Lee
Felix von Drigalski
Yoshihisa Ijiri

Practical industrial assembly scenarios often require robotic agents to adapt their skills to unseen tasks quickly. While transfer reinforcement learning (RL) could enable such quick adaptation, much prior work has to collect many samples from source environments to learn target tasks in a model-free fashion, which still lacks sample efficiency on a practical level. In this work, we develop a novel transfer RL method named TRANSfer learning by Aggregating dynamics Models (TRANS-AM). TRANS-AM is based on model-based RL (MBRL) for its high-level sample efficiency, and only requires dynamics models to be collected from source environments. Specifically, it learns to aggregate source dynamics models adaptively in an MBRL loop to better fit the state-transition dynamics of target environments and execute optimal actions there. As a case study to show the effectiveness of this proposed approach, we address a challenging contact-rich peg-in-hole task with variable hole orientations using a soft robot. Our evaluations with both simulation and real-robot experiments demonstrate that TRANS-AM enables the soft robot to accomplish target tasks with fewer episodes compared when learning the tasks from scratch.

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IROS Conference 2020 Conference Paper

A Compact, Cable-driven, Activatable Soft Wrist with Six Degrees of Freedom for Assembly Tasks

Felix von Drigalski
Kazutoshi Tanaka
Masashi Hamaya
Robert Lee
Chisato Nakashima
Yoshiya Shibata
Yoshihisa Ijiri

Physical softness has been proposed to absorb impacts when establishing contact with a robot or its workpiece, to relax control requirements and improve performance in assembly and insertion tasks. Previous work has focused on special end effector solutions for isolated tasks, such as the peg-in-hole task. However, as many robot tasks require the precision of rigid robots, and their performance would degrade when simply adding compliance, it has been difficult to take advantage of physical softness in real applications. A wrist that could switch between soft and rigid modes could solve this problem, but actuators with sufficient strength for this state transition would increase the size and weight of the module and decrease the payload of the robot. To solve this problem, we propose a novel design of a soft module consisting of a cable-driven mechanism, which allows the robot end effector to change between soft and rigid mode while being very compact and light. The module effectively combines the advantages of soft and rigid robots, and can be retrofitted to existing robots and grippers while preserving the characteristics of the robotic system. We evaluate the effectiveness of our proposed design through experiments modeling assembly tasks, and investigate design parameters quantitatively.

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ICRA Conference 2020 Conference Paper

Contact-based in-hand pose estimation using Bayesian state estimation and particle filtering

Felix von Drigalski
Shohei Taniguchi
Robert Lee
Takamitsu Matsubara
Masashi Hamaya
Kazutoshi Tanaka
Yoshihisa Ijiri

In industrial assembly tasks, the position of an object grasped by the robot has to be known with high precision in order to insert or place it. In real applications, this problem is commonly solved by jigs that are specially produced for each part. However, they significantly limit flexibility and are prohibitive when the target parts change often, so a flexible method to localize parts with high accuracy after grasping is desired. To solve this problem, we propose a method that can estimate the position of an object in the robot's hand to sub-millimeter precision, and can improve its estimate incrementally, using only minimal calibration and a force sensor. Our method is applicable to any robotic gripper and any rigid object that the gripper can hold, and requires only a force sensor. We demonstrate that the method can determine the position of an object to a precision of under 1 mm without using any part-specific jigs or equipment.

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ICRA Conference 2020 Conference Paper

Learning Robotic Assembly Tasks with Lower Dimensional Systems by Leveraging Physical Softness and Environmental Constraints

Masashi Hamaya
Robert Lee
Kazutoshi Tanaka
Felix von Drigalski
Chisato Nakashima
Yoshiya Shibata
Yoshihisa Ijiri

In this study, we present a novel control framework for assembly tasks with a soft robot. Typically, existing hard robots require high frequency controllers and precise force/torque sensors for assembly tasks. The resulting robot system is complex, entailing large amounts of engineering and maintenance. Physical softness allows the robot to interact with the environment easily. We expect soft robots to perform assembly tasks without the need for high frequency force/torque controllers and sensors. However, specific data-driven approaches are needed to deal with complex models involving nonlinearity and hysteresis. If we were to apply these approaches directly, we would be required to collect very large amounts of training data. To solve this problem, we argue that by leveraging softness and environmental constraints, a robot can complete tasks in lower dimensional state and action spaces, which could greatly facilitate the exploration of appropriate assembly skills. Then, we apply a highly efficient model-based reinforcement learning method to lower dimensional systems. To verify our method, we perform a simulation for peg-in-hole tasks. The results show that our method learns the appropriate skills faster than an approach that does not consider lower dimensional systems. Moreover, we demonstrate that our method works on a real robot equipped with a compliant module on the wrist.

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IROS Conference 2020 Conference Paper

Learning Soft Robotic Assembly Strategies from Successful and Failed Demonstrations

Masashi Hamaya
Felix von Drigalski
Takamitsu Matsubara
Kazutoshi Tanaka
Robert Lee
Chisato Nakashima
Yoshiya Shibata
Yoshihisa Ijiri

Physically soft robots are promising for robotic assembly tasks as they allow stable contacts with the environment. In this study, we propose a novel learning system for soft robotic assembly strategies. We formulate this problem as a reinforcement learning task and design the reward function from human demonstrations. Our key insight is that the failed demonstrations can be used as constraints to avoid failed behaviors. To this end, we developed a teaching device with which humans can intuitively provide various demonstrations. Moreover, we leverage Physically-Consistent Gaussian Mixture Models to clearly assign Gaussian components to the successful and failed trials. We then create the reference trajectories via Gaussian Mixture Regressions, which fit the successful demonstrations while considering the failed ones. Finally, we apply a sample- efficient deep model-based reinforcement learning method to obtain robust strategies with a few interactions. To validate our method, we developed a real-robot experimental system composed of a rigid collaborative robot arm with a compliant wrist and the teaching device. Our results demonstrated that our method learned the assembly strategies with a higher success rate than when using only successful demonstrations.

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