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Richard Cheng

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14 papers

2 author rows

ICRA Conference 2025 Conference Paper

Persistent Object Gaussian Splat (POGS) for Tracking Human and Robot Manipulation of Irregularly Shaped Objects

Justin Yu
Kush Hari
Karim El-Refai
Arnav Dalal
Justin Kerr
Chung Min Kim
Richard Cheng
Muhammad Zubair Irshad

Tracking and manipulating irregularly-shaped, previously unseen objects in dynamic environments is important for robotic applications in manufacturing, assembly, and logistics. Recently introduced Gaussian Splats [1] efficiently model object geometry, but lack persistent state estimation for taskoriented manipulation. We present Persistent Object Gaussian Splat (POGS), a system that embeds semantics, self-supervised visual features, and object grouping features into a compact representation that can be continuously updated to estimate the pose of scanned objects. POGS updates object states without requiring expensive rescanning or prior CAD models of objects. After an initial multi-view scene capture and training phase, POGS uses a single stereo camera to integrate depth estimates along with self-supervised vision encoder features for object pose estimation. POGS supports grasping, reorientation, and natural language-driven manipulation by refining object pose estimates, facilitating sequential object reset operations with human-induced object perturbations and tool servoing, where robots recover tool pose despite tool perturbations of up to 30°. POGS achieves up to 12 consecutive successful object resets and recovers from 80% of in-grasp tool perturbations.

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IROS Conference 2024 Conference Paper

A Direct Semi-Exhaustive Search Method for Robust, Partial-to-Full Point Cloud Registration

Richard Cheng
Chavdar Papazov
Daniel M. Helmick
Mark Tjersland

Point cloud registration refers to the problem of finding the rigid transformation that aligns two given point clouds, and is crucial for many applications in robotics and computer vision. The main insight of this paper is that we can directly optimize the point cloud registration problem without correspondences by utilizing an algorithmically simple, yet computationally complex, semi-exhaustive search approach that is very well-suited for parallelization on modern GPUs. Our proposed algorithm, Direct Semi-Exhaustive Search (DSES), iterates over potential rotation matrices and efficiently computes the inlier-maximizing translation associated with each rotation. It then computes the optimal rigid transformation based on any desired distance metric by directly computing the error associated with each transformation candidate {R, t}. By leveraging the parallelism of modern GPUs, DSES outperforms state-of-the-art methods for partial-to-full point cloud registration on the simulated ModelNet40 benchmark and demonstrates high performance and robustness for pose estimation on a real-world robotics problem (https://youtu.be/q0q2-s2KSuA).

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IROS Conference 2024 Conference Paper

Language-Embedded Gaussian Splats (LEGS): Incrementally Building Room-Scale Representations with a Mobile Robot

Justin Yu
Kush Hari
Kishore Srinivas
Karim El-Refai
Adam Rashid
Chung Min Kim
Justin Kerr
Richard Cheng

Building semantic 3D maps is valuable for searching for objects of interest in offices, warehouses, stores, and homes. We present a mapping system that incrementally builds a Language-Embedded Gaussian Splat (LEGS): a detailed 3D scene representation that encodes both appearance and semantics in a unified representation. LEGS is trained online as a robot traverses its environment to enable localization of open-vocabulary object queries. We evaluate LEGS on 4 room-scale scenes where we query for objects in the scene to assess how LEGS can capture semantic meaning. We compare LEGS to LERF [1] and find that while both systems have comparable object query success rates, LEGS trains over 3. 5x faster than LERF. Results suggest that a multi-camera setup and incremental bundle adjustment can boost visual reconstruction quality in constrained robot trajectories, and suggest LEGS can localize open-vocabulary and long-tail object queries with up to 66% accuracy. See project website at: berkeleyautomation.github.io/LEGS

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IROS Conference 2024 Conference Paper

MANIP: A Modular Architecture for Integrating Interactive Perception for Robot Manipulation

Justin Yu
Tara Sadjadpour
Abby O'Neill
Mehdi Khfifi
Lawrence Yunliang Chen
Richard Cheng
Muhammad Zubair Irshad
Ashwin Balakrishna

We propose a modular systems architecture, MANIP, that can facilitate the design and development of robot manipulation systems by systematically combining learned subpolicies with well-established procedural algorithmic primitives such as Inverse Kinematics, Kalman Filters, RANSAC outlier rejection, PID modules, etc. (aka "Good Old Fashioned Engineering (GOFE)"). The MANIP architecture grew from our lab’s experience developing robot systems for folding clothes, routing cables, and untangling knots. To address failure modes, MANIP can facilitate inclusion of "interactive perception" subpolicies that execute robot actions to modify system state to bring the system into alignment with the training distribution and / or to disambiguate system state when system state confidence is low. We demonstrate how MANIP can be applied with 3 case studies and then describe a detailed case study in cable tracing with experiments that suggest MANIP can improve performance by up to 88%. Code and details are available at: https://berkeleyautomation.github.io/MANIP/

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ICRA Conference 2023 Conference Paper

AutoBag: Learning to Open Plastic Bags and Insert Objects

Lawrence Yunliang Chen
Baiyu Shi
Daniel Seita
Richard Cheng
Thomas Kollar
David Held
Ken Goldberg

Thin plastic bags are ubiquitous in retail stores, healthcare, food handling, recycling, homes, and school lunchrooms. They are challenging both for perception (due to specularities and occlusions) and for manipulation (due to the dynamics of their 3D deformable structure). We formulate the task of “bagging: ” manipulating common plastic shopping bags with two handles from an unstructured initial state to an open state where at least one solid object can be inserted into the bag and lifted for transport. We propose a self-supervised learning framework where a dual-arm robot learns to recognize the handles and rim of plastic bags using UV-fluorescent markings; at execution time, the robot does not use UV markings or UV light. We propose the AutoBag algorithm, where the robot uses the learned perception model to open a plastic bag through iterative manipulation. We present novel metrics to evaluate the quality of a bag state and new motion primitives for reorienting and opening bags based on visual observations. In physical experiments, a YuMi robot using AutoBag is able to open bags and achieve a success rate of 16/30 for inserting at least one item across a variety of initial bag configurations. Supplementary material is available at https://sites.google.com/view/autobag.

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IROS Conference 2023 Conference Paper

Bagging by Learning to Singulate Layers Using Interactive Perception

Lawrence Yunliang Chen
Baiyu Shi
Roy Lin
Daniel Seita
Ayah Ahmad
Richard Cheng
Thomas Kollar
David Held

Many fabric handling and 2D deformable material tasks in homes and industries require singulating layers of material such as opening a bag or arranging garments for sewing. In contrast to methods requiring specialized sensing or end effectors, we use only visual observations with ordinary parallel jaw grippers. We propose SLIP: Singulating Layers using Interactive Perception, and apply SLIP to the task of autonomous bagging. We develop SLIP-Bagging, a bagging algorithm that manipulates a plastic or fabric bag from an unstructured state and uses SLIP to grasp the top layer of the bag to open it for object insertion. In physical experiments, a YuMi robot achieves a success rate of 67% to 81% across bags of a variety of materials, shapes, and sizes, significantly improving in success rate and generality over prior work. Experiments also suggest that SLIP can be applied to tasks such as singulating layers of folded cloth and garments. Supplementary material is available at https://sites.google.com/view/slip-bagging/.

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ICRA Conference 2023 Conference Paper

SGTM 2. 0: Autonomously Untangling Long Cables using Interactive Perception

Kaushik Shivakumar
Vainavi Viswanath
Anrui Gu
Yahav Avigal
Justin Kerr
Jeffrey Ichnowski
Richard Cheng
Thomas Kollar

Cables are commonplace in homes, hospitals, and industrial warehouses and are prone to tangling. This paper extends prior work on autonomously untangling long cables by introducing novel uncertainty quantification metrics and actions that interact with the cable to reduce perception uncertainty. We present Sliding and Grasping for Tangle Manipulation 2. 0 (SGTM 2. 0), a system that autonomously untangles cables approximately 3 meters in length with a bilateral robot using estimates of uncertainty at each step to inform actions. By interactively reducing uncertainty, SGTM 2. 0 significantly reduces run-time. Physical experiments with 84 trials suggest that SGTM $2. 0$ can achieve 83% untangling success on cables with 1 or 2 overhand and figure-8 knots, and 70% termination detection success across these configurations, outperforming SGTM 1. 0 by 43% in untangling accuracy and 200% in completion time. Supplementary material, visualizations, and videos can be found at sites.google.com/view/sgtm2.

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ICRA Conference 2021 Conference Paper

Limits of Probabilistic Safety Guarantees when Considering Human Uncertainty

Richard Cheng
Richard M. Murray
Joel W. Burdick

When autonomous robots interact with humans, such as during autonomous driving, explicit safety guarantees are crucial in order to avoid potentially life-threatening accidents. Many data-driven methods have explored learning probabilistic bounds over human agents’ trajectories (i. e. confidence tubes that contain trajectories with probability δ), which can then be used to guarantee safety with probability 1− δ. However, almost all existing works consider δ ≥ 0. 001. The purpose of this paper is to argue that (1) in safety-critical applications, it is necessary to provide safety guarantees with δ −8, and (2) current learning-based methods are illequipped to compute accurate confidence bounds at such low δ. Using human driving data (from the highD dataset), as well as synthetically generated data, we show that current uncertainty models use inaccurate distributional assumptions to describe human behavior and/or require infeasible amounts of data to accurately learn confidence bounds for δ ≤ 10 −8. These two issues result in unreliable confidence bounds, which can have dangerous implications if deployed on safety-critical systems.

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IROS Conference 2020 Conference Paper

Human Preference-Based Learning for High-dimensional Optimization of Exoskeleton Walking Gaits

Maegan Tucker
Myra Cheng
Ellen R. Novoseller
Richard Cheng
Yisong Yue
Joel W. Burdick
Aaron D. Ames

Optimizing lower-body exoskeleton walking gaits for user comfort requires understanding users' preferences over a high-dimensional gait parameter space. However, existing preference-based learning methods have only explored low-dimensional domains due to computational limitations. To learn user preferences in high dimensions, this work presents LINECOSPAR, a human-in-the-loop preference-based framework that enables optimization over many parameters by iteratively exploring one-dimensional subspaces. Additionally, this work identifies gait attributes that characterize broader preferences across users. In simulations and human trials, we empirically verify that LINECOSPAR is a sample-efficient approach for high-dimensional preference optimization. Our analysis of the experimental data reveals a correspondence between human preferences and objective measures of dynamicity, while also highlighting differences in the utility functions underlying individual users' gait preferences. This result has implications for exoskeleton gait synthesis, an active field with applications to clinical use and patient rehabilitation.

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IROS Conference 2020 Conference Paper

Learning an Optimal Sampling Distribution for Efficient Motion Planning

Richard Cheng
Krishna Shankar
Joel W. Burdick

Sampling-based motion planners (SBMP) are commonly used to generate motion plans by incrementally constructing a search tree through a robot's configuration space. For high degree-of-freedom systems, sampling is often done in a lower-dimensional space, with a steering function responsible for local planning in the higher-dimensional configuration space. However, for highly-redundant systems with complex kinematics, this approach is problematic due to the high computational cost of evaluating the steering function, especially in cluttered environments. Therefore, having an efficient, informed sampler becomes critical to online robot operation. In this study, we develop a learning-based approach with policy improvement to compute an optimal sampling distribution for use in SBMPs. Motivated by the challenge of whole-body planning for a 31 degree-of-freedom mobile robot built by the Toyota Research Institute, we combine our learning-based approach with classical graph-search to obtain a constrained sampling distribution. Over multiple learning iterations, the algorithm learns a probability distribution weighting areas of low-cost and high probability of success, which a graph search algorithm then uses to obtain an optimal sampling distribution for the robot. On challenging motion planning tasks for the robot, we observe significant computational speed-up, fewer edge evaluations, and more efficient paths with minimal computational overhead. We show the efficacy of our approach with a number of experiments in whole-body motion planning.

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ICML Conference 2019 Conference Paper

Control Regularization for Reduced Variance Reinforcement Learning

Richard Cheng
Abhinav Verma 0001
Gábor Orosz
Swarat Chaudhuri
Yisong Yue
Joel W. Burdick

Dealing with high variance is a significant challenge in model-free reinforcement learning (RL). Existing methods are unreliable, exhibiting high variance in performance from run to run using different initializations/seeds. Focusing on problems arising in continuous control, we propose a functional regularization approach to augmenting model-free RL. In particular, we regularize the behavior of the deep policy to be similar to a policy prior, i. e. , we regularize in function space. We show that functional regularization yields a bias-variance trade-off, and propose an adaptive tuning strategy to optimize this trade-off. When the policy prior has control-theoretic stability guarantees, we further show that this regularization approximately preserves those stability guarantees throughout learning. We validate our approach empirically on a range of settings, and demonstrate significantly reduced variance, guaranteed dynamic stability, and more efficient learning than deep RL alone.

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AAAI Conference 2019 Conference Paper

End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks

Richard Cheng
Gábor Orosz
Richard M. Murray
Joel W. Burdick

Reinforcement Learning (RL) algorithms have found limited success beyond simulated applications, and one main reason is the absence of safety guarantees during the learning process. Real world systems would realistically fail or break before an optimal controller can be learned. To address this issue, we propose a controller architecture that combines (1) a model-free RL-based controller with (2) model-based controllers utilizing control barrier functions (CBFs) and (3) online learning of the unknown system dynamics, in order to ensure safety during learning. Our general framework leverages the success of RL algorithms to learn high-performance controllers, while the CBF-based controllers both guarantee safety and guide the learning process by constraining the set of explorable polices. We utilize Gaussian Processes (GPs) to model the system dynamics and its uncertainties. Our novel controller synthesis algorithm, RL-CBF, guarantees safety with high probability during the learning process, regardless of the RL algorithm used, and demonstrates greater policy exploration efficiency. We test our algorithm on (1) control of an inverted pendulum and (2) autonomous carfollowing with wireless vehicle-to-vehicle communication, and show that our algorithm attains much greater sample efficiency in learning than other state-of-the-art algorithms and maintains safety during the entire learning process.

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IROS Conference 2018 Conference Paper

On Muscle Activation for Improving Robotic Rehabilitation after Spinal Cord Injury

Richard Cheng
Yanan Sui
Dimitry Sayenko
Joel W. Burdick

Spinal cord stimulation (SCS) has recently enabled humans with motor complete spinal cord injury (SCI) to independently stand and recover some lost autonomic function. However, the nature of the recovered motor activity and the interplay between SCS and motor training are not well understood. Understanding the effect of stand training and spinal stimulation on motor activity during bipedal standing is important for designing spinal rehabilitation therapies that seek to combine spinal stimulation and rehabilitative robots. In this study, we examined electromyography (EMG) data gathered from two SCI patients and six healthy subjects as they attempted standing. We analyzed the muscle activation patterns and EMG waveform shape to quantify both the changes in SCI patient motor activity with training, and the differences between healthy motor activity and SCI patient motor activity under stimulation. We also looked for correlations between the similarity in SCI patients' motor activity to healthy subjects and their overall standing ability. We found that good standing in SCI patients does not emulate healthy standing muscle activity. Furthermore, patient stand training heavily influenced motor activation patterns, but not in ways that improved standing ability. These results indicate that current training techniques do not optimally influence motor activity, and robotic rehabilitation strategies for SCI patients should target essential features of motor activity to optimize functional performance, rather than emulate healthy activity.

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IROS Conference 2015 Conference Paper

Wind disturbance rejection for an insect-scale flapping-wing robot

Pakpong Chirarattananon
Kevin Y. Ma
Richard Cheng
Robert J. Wood

Despite having achieved unconstrained stable flight, the insect-scale flapping-wing robot is still tethered for power and control. Towards the goal of operating a biologically-inspired robot autonomously outside of laboratory conditions. In this paper, we simulate outdoor disturbances in the laboratory setting and investigate the effects of wind gusts on the flight dynamics of a millimeter-scale flapping wing robot. Simplified models describing the disturbance effects on the robot's dynamics are proposed, together with two disturbance rejection schemes capable of estimating and compensating for the disturbances. The proposed methods are experimentally verified. The results show that they reduced the root mean square position errors by approximately 50% when the robot was subject to 60 cm·s −1 horizontal wind.

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