4 citations · 10 across the 19 of their papers we have counts for
9 papers · 1 filter
On-Robot Reinforcement Learning with Goal-Contrastive Rewards
Ondrej Biza, Thomas Weng, Lingfeng Sun +6
Reinforcement Learning (RL) has the potential to enable robots to learn from their own actions in the real world. Unfortunately, RL can be prohibitively expensive, in terms of on-r…
MATCH POLICY: A Simple Pipeline from Point Cloud Registration to Manipulation Policies
Haojie Huang, Haotian Liu, Dian Wang +2
Many manipulation tasks require the robot to rearrange objects relative to one another. Such tasks can be described as a sequence of relative poses between parts of a set of rigid…
Equivariant Reinforcement Learning under Partial Observability
Hai Nguyen, Andrea Baisero, David Klee +3
Incorporating inductive biases is a promising approach for tackling challenging robot learning domains with sample-efficient solutions. This paper identifies partially observable d…
ThinkGrasp: A Vision-Language System for Strategic Part Grasping in Clutter
Yaoyao Qian, Xupeng Zhu, Ondrej Biza +5
Robotic grasping in cluttered environments remains a significant challenge due to occlusions and complex object arrangements. We have developed ThinkGrasp, a plug-and-play vision-l…
OrbitGrasp: -Equivariant Grasp Learning
Boce Hu, Xupeng Zhu, Dian Wang +5
While grasp detection is an important part of any robotic manipulation pipeline, reliable and accurate grasp detection in remains a research challenge. Many robotics applic…
Equivariant Offline Reinforcement Learning
Arsh Tangri, Ondrej Biza, Dian Wang +3
Sample efficiency is critical when applying learning-based methods to robotic manipulation due to the high cost of collecting expert demonstrations and the challenges of on-robot p…
Learning Efficient and Robust Language-conditioned Manipulation using Textual-Visual Relevancy and Equivariant Language Mapping
Mingxi Jia, Haojie Huang, Zhewen Zhang +7
Controlling robots through natural language is pivotal for enhancing human-robot collaboration and synthesizing complex robot behaviors. Recent works that are trained on large robo…
Imagination Policy: Using Generative Point Cloud Models for Learning Manipulation Policies
Haojie Huang, Karl Schmeckpeper, Dian Wang +6
Humans can imagine goal states during planning and perform actions to match those goals. In this work, we propose Imagination Policy, a novel multi-task key-frame policy network fo…
Fourier Transporter: Bi-Equivariant Robotic Manipulation in 3D
Haojie Huang, Owen Howell, Dian Wang +3
Many complex robotic manipulation tasks can be decomposed as a sequence of pick and place actions. Training a robotic agent to learn this sequence over many different starting cond…