7 citations · 32 across the 12 of their papers we have counts for
12 papers
Robot Learning with Super-Linear Scaling
Marcel Torne, Arhan Jain, Jiayi Yuan +5
Scaling robot learning requires data collection pipelines that scale favorably with human effort. In this work, we propose Crowdsourcing and Amortizing Human Effort for Real-to-Sim…
Diffusion Policy Policy Optimization
Allen Z. Ren, Justin Lidard, Lars L. Ankile +6
We introduce Diffusion Policy Policy Optimization, DPPO, an algorithmic framework including best practices for fine-tuning diffusion-based policies (e.g. Diffusion Policy) in conti…
From Imitation to Refinement -- Residual RL for Precise Assembly
Lars Ankile, Anthony Simeonov, Idan Shenfeld +2
Recent advances in Behavior Cloning (BC) have made it easy to teach robots new tasks. However, we find that the ease of teaching comes at the cost of unreliable performance that sa…
JUICER: Data-Efficient Imitation Learning for Robotic Assembly
Lars Ankile, Anthony Simeonov, Idan Shenfeld +1
While learning from demonstrations is powerful for acquiring visuomotor policies, high-performance imitation without large demonstration datasets remains challenging for tasks requ…
Reconciling Reality through Simulation: A Real-to-Sim-to-Real Approach for Robust Manipulation
Marcel Torne, Anthony Simeonov, Zechu Li +4
Imitation learning methods need significant human supervision to learn policies robust to changes in object poses, physical disturbances, and visual distractors. Reinforcement lear…
Lifelong Robot Learning with Human Assisted Language Planners
Meenal Parakh, Alisha Fong, Anthony Simeonov +3
Large Language Models (LLMs) have been shown to act like planners that can decompose high-level instructions into a sequence of executable instructions. However, current LLM-based…
Shelving, Stacking, Hanging: Relational Pose Diffusion for Multi-modal Rearrangement
Anthony Simeonov, Ankit Goyal, Lucas Manuelli +5
We propose a system for rearranging objects in a scene to achieve a desired object-scene placing relationship, such as a book inserted in an open slot of a bookshelf. The pipeline…
Local Neural Descriptor Fields: Locally Conditioned Object Representations for Manipulation
Ethan Chun, Yilun Du, Anthony Simeonov +2
A robot operating in a household environment will see a wide range of unique and unfamiliar objects. While a system could train on many of these, it is infeasible to predict all th…
SE(3)-Equivariant Relational Rearrangement with Neural Descriptor Fields
Anthony Simeonov, Yilun Du, Lin Yen-Chen +4
We present a method for performing tasks involving spatial relations between novel object instances initialized in arbitrary poses directly from point cloud observations. Our frame…
Neural Descriptor Fields: SE(3)-Equivariant Object Representations for Manipulation
Anthony Simeonov, Yilun Du, Andrea Tagliasacchi +4
We present Neural Descriptor Fields (NDFs), an object representation that encodes both points and relative poses between an object and a target (such as a robot gripper or a rack u…
A Long Horizon Planning Framework for Manipulating Rigid Pointcloud Objects
Anthony Simeonov, Yilun Du, Beomjoon Kim +4
We present a framework for solving long-horizon planning problems involving manipulation of rigid objects that operates directly from a point-cloud observation, i.e. without prior…
Motion Planning Networks: Bridging the Gap Between Learning-based and Classical Motion Planners
Ahmed H. Qureshi, Yinglong Miao, Anthony Simeonov +1
This paper describes Motion Planning Networks (MPNet), a computationally efficient, learning-based neural planner for solving motion planning problems. MPNet uses neural networks t…