[00:00] Paper intro: Zero-shot robotic manipulation via local policies

[00:26] Key challenges: Limited generalization and sim-to-real transfer

[01:03] Local policies: Task decomposition through localized focus regions

[01:38] Foundation models: VLMs for task understanding

[02:07] Training approach: Simulation-based RL + visuomotor policy distillation

[02:46] Implementation: Depth maps and impedance control system

[03:25] Results: 97% simulation success, 76% real-world success

[04:02] Challenges: Vision errors and collision handling

[04:32] Limitations: Issues with reflective objects and complex contacts

[05:48] Impact: Advancing autonomous robotic manipulation

[06:36] Design: Modular system for continuous improvement

[07:21] Dependencies: VLM and motion planner requirements

Authors: Murtaza Dalal, Min Liu, Walter Talbott, Chen Chen, Deepak Pathak, Jian Zhang, Ruslan Salakhutdinov

Affiliations: Carnegie Mellon University, Apple

Abstract: Sim2real for robotic manipulation is difficult due to the challenges of simulating complex contacts and generating realistic task distributions. To tackle the latter problem, we introduce ManipGen, which leverages a new class of policies for sim2real transfer: local policies. Locality enables a variety of appealing properties including invariances to absolute robot and object pose, skill ordering, and global scene configuration. We combine these policies with foundation models for vision, language and motion planning and demonstrate SOTA zero-shot performance of our method to Robosuite benchmark tasks in simulation (97%). We transfer our local policies from simulation to reality and observe they can solve unseen long-horizon manipulation tasks with up to 8 stages with significant pose, object and scene configuration variation. ManipGen outperforms SOTA approaches such as SayCan, OpenVLA, LLMTrajGen and VoxPoser across 50 real-world manipulation tasks by 36%, 76%, 62% and 60% respectively. Video results at this https URL

Link: https://mihdalal.github.io/manipgen/