4 citations · 10 across the 19 of their papers we have counts for
20 papers
Pix2Act: Image-Space Manipulation Policies with Equivariant Augmentation
Haojie Huang, Linfeng Zhao, Haotian Liu +9
Representing manipulation actions as 2D trajectories in the camera plane provides a compact and interpretable basis for learning complex 3D manipulation policies. However, it also…
Action Map Policy: Learning 3D Closed-loop Manipulation via Pixel Classification
Haojie Huang, Zhang Ye, Linfeng Zhao +7
The action space poses a major challenge in robot learning, since it is often high-dimensional, can span long time horizons, and frequently admits multi-modal optimal solutions. A…
Residual Rotation Correction using Tactile Equivariance
Yizhe Zhu, Zhang Ye, Boce Hu +4
Visuotactile policy learning augments vision-only policies with tactile input, facilitating contact-rich manipulation. However, the high cost of tactile data collection makes sampl…
Generalizable Hierarchical Skill Learning via Object-Centric Representation
Haibo Zhao, Yu Qi, Boce Hu +9
We present Generalizable Hierarchical Skill Learning (GSL), a novel framework for hierarchical policy learning that significantly improves policy generalization and sample efficien…
Clebsch-Gordan Transformer: Fast and Global Equivariant Attention
Owen Lewis Howell, Linfeng Zhao, Xupeng Zhu +6
The global attention mechanism is one of the keys to the success of transformer architecture, but it incurs quadratic computational costs in relation to the number of tokens. On th…
EquAct: An SE(3)-Equivariant Multi-Task Transformer for Open-Loop Robotic Manipulation
Xupeng Zhu, Yu Qi, Yizhe Zhu +2
Transformer architectures can effectively learn language-conditioned, multi-task 3D open-loop manipulation policies from demonstrations by jointly processing natural language instr…
3D Equivariant Visuomotor Policy Learning via Spherical Projection
Boce Hu, Dian Wang, David Klee +5
Equivariant models have recently been shown to improve the data efficiency of diffusion policy by a significant margin. However, prior work that explored this direction focused pri…
A Practical Guide for Incorporating Symmetry in Diffusion Policy
Dian Wang, Boce Hu, Shuran Song +2
Recently, equivariant neural networks for policy learning have shown promising improvements in sample efficiency and generalization, however, their wide adoption faces substantial…
Push-Grasp Policy Learning Using Equivariant Models and Grasp Score Optimization
Boce Hu, Heng Tian, Dian Wang +4
Goal-conditioned robotic grasping in cluttered environments remains a challenging problem due to occlusions caused by surrounding objects, which prevent direct access to the target…
Hierarchical Equivariant Policy via Frame Transfer
Haibo Zhao, Dian Wang, Yizhe Zhu +6
Recent advances in hierarchical policy learning highlight the advantages of decomposing systems into high-level and low-level agents, enabling efficient long-horizon reasoning and…
Coarse-to-Fine 3D Keyframe Transporter
Xupeng Zhu, David Klee, Dian Wang +5
Recent advances in Keyframe Imitation Learning (IL) have enabled learning-based agents to solve a diverse range of manipulation tasks. However, most approaches ignore the rich symm…
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…