269 citations · 2k across the 61 of their papers we have counts for
55 papers · 1 filter
Open X-Embodiment: Robotic Learning Datasets and RT-X Models
Embodiment Collaboration, Abby O'Neill, Abdul Rehman +291
Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, thi…
Multi-Stage Cable Routing through Hierarchical Imitation Learning
Jianlan Luo, Charles Xu, Xinyang Geng +5
We study the problem of learning to perform multi-stage robotic manipulation tasks, with applications to cable routing, where the robot must route a cable through a series of clips…
Prim-LAfD: A Framework to Learn and Adapt Primitive-Based Skills from Demonstrations for Insertion Tasks
Zheng Wu, Wenzhao Lian, Changhao Wang +3
Learning generalizable insertion skills in a data-efficient manner has long been a challenge in the robot learning community. While the current state-of-the-art methods with reinfo…
Zero-Shot Policy Transfer with Disentangled Task Representation of Meta-Reinforcement Learning
Zheng Wu, Yichen Xie, Wenzhao Lian +5
Humans are capable of abstracting various tasks as different combinations of multiple attributes. This perspective of compositionality is vital for human rapid learning and adaptio…
A System for Imitation Learning of Contact-Rich Bimanual Manipulation Policies
Simon Stepputtis, Maryam Bandari, Stefan Schaal +1
In this paper, we discuss a framework for teaching bimanual manipulation tasks by imitation. To this end, we present a system and algorithms for learning compliant and contact-rich…
Detection and Physical Interaction with Deformable Linear Objects
Azarakhsh Keipour, Mohammadreza Mousaei, Maryam Bandari +2
Deformable linear objects (e.g., cables, ropes, and threads) commonly appear in our everyday lives. However, perception of these objects and the study of physical interaction with…
Symbolic State Estimation with Predicates for Contact-Rich Manipulation Tasks
Toki Migimatsu, Wenzhao Lian, Jeannette Bohg +1
Manipulation tasks often require a robot to adjust its sensorimotor skills based on the state it finds itself in. Taking peg-in-hole as an example: once the peg is aligned with the…
Efficient Spatial Representation and Routing of Deformable One-Dimensional Objects for Manipulation
Azarakhsh Keipour, Maryam Bandari, Stefan Schaal
With the field of rigid-body robotics having matured in the last fifty years, routing, planning, and manipulation of deformable objects have recently emerged as a more untouched re…
You Only Demonstrate Once: Category-Level Manipulation from Single Visual Demonstration
Bowen Wen, Wenzhao Lian, Kostas Bekris +1
Promising results have been achieved recently in category-level manipulation that generalizes across object instances. Nevertheless, it often requires expensive real-world data col…
Wish you were here: Hindsight Goal Selection for long-horizon dexterous manipulation
Todor Davchev, Oleg Sushkov, Jean-Baptiste Regli +4
Complex sequential tasks in continuous-control settings often require agents to successfully traverse a set of "narrow passages" in their state space. Solving such tasks with a spa…
From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence
Nicholas Roy, Ingmar Posner, Tim Barfoot +17
Machine learning has long since become a keystone technology, accelerating science and applications in a broad range of domains. Consequently, the notion of applying learning metho…
Offline Meta-Reinforcement Learning for Industrial Insertion
Tony Z. Zhao, Jianlan Luo, Oleg Sushkov +5
Reinforcement learning (RL) can in principle let robots automatically adapt to new tasks, but current RL methods require a large number of trials to accomplish this. In this paper,…
CaTGrasp: Learning Category-Level Task-Relevant Grasping in Clutter from Simulation
Bowen Wen, Wenzhao Lian, Kostas Bekris +1
Task-relevant grasping is critical for industrial assembly, where downstream manipulation tasks constrain the set of valid grasps. Learning how to perform this task, however, is ch…
A Robustness Analysis of Inverse Optimal Control of Bipedal Walking
John R. Rebula, Stefan Schaal, James Finley +1
Cost functions have the potential to provide compact and understandable generalizations of motion. The goal of Inverse Optimal Control (IOC) is to analyze an observed behavior whic…
Benchmarking Off-The-Shelf Solutions to Robotic Assembly Tasks
Wenzhao Lian, Tim Kelch, Dirk Holz +2
In recent years, many learning based approaches have been studied to realize robotic manipulation and assembly tasks, often including vision and force/tactile feedback. However, it…
Learning Dense Rewards for Contact-Rich Manipulation Tasks
Zheng Wu, Wenzhao Lian, Vaibhav Unhelkar +2
Rewards play a crucial role in reinforcement learning. To arrive at the desired policy, the design of a suitable reward function often requires significant domain expertise as well…
Residual Learning from Demonstration: Adapting DMPs for Contact-rich Manipulation
Todor Davchev, Kevin Sebastian Luck, Michael Burke +3
Manipulation skills involving contact and friction are inherent to many robotics tasks. Using the class of motor primitives for peg-in-hole like insertions, we study how robots can…
An Interior Point Method Solving Motion Planning Problems with Narrow Passages
Jim Mainprice, Nathan Ratliff, Marc Toussaint +1
Algorithmic solutions for the motion planning problem have been investigated for five decades. Since the development of A* in 1969 many approaches have been investigated, tradition…
Supervised Learning and Reinforcement Learning of Feedback Models for Reactive Behaviors: Tactile Feedback Testbed
Giovanni Sutanto, Katharina Rombach, Yevgen Chebotar +4
Robots need to be able to adapt to unexpected changes in the environment such that they can autonomously succeed in their tasks. However, hand-designing feedback models for adaptat…
Simulator Predictive Control: Using Learned Task Representations and MPC for Zero-Shot Generalization and Sequencing
Zhanpeng He, Ryan Julian, Eric Heiden +5
Simulation-to-real transfer is an important strategy for making reinforcement learning practical with real robots. Successful sim-to-real transfer systems have difficulty producing…
Learning Task-Specific Dynamics to Improve Whole-Body Control
Andrej Gams, Sean A. Mason, Aleš Ude +2
In task-based inverse dynamics control, reference accelerations used to follow a desired plan can be broken down into feedforward and feedback trajectories. The feedback term accou…
An MPC Walking Framework With External Contact Forces
Sean Mason, Nicholas Rotella, Stefan Schaal +1
In this work, we present an extension to a linear Model Predictive Control (MPC) scheme that plans external contact forces for the robot when given multiple contact locations and t…
A New Data Source for Inverse Dynamics Learning
Daniel Kappler, Franziska Meier, Nathan Ratliff +1
Modern robotics is gravitating toward increasingly collaborative human robot interaction. Tools such as acceleration policies can naturally support the realization of reactive, ada…
Learning Sensor Feedback Models from Demonstrations via Phase-Modulated Neural Networks
Giovanni Sutanto, Zhe Su, Stefan Schaal +1
In order to robustly execute a task under environmental uncertainty, a robot needs to be able to reactively adapt to changes arising in its environment. The environment changes are…