This document serves as a position paper that outlines the authors' vision for a potential
pathway towards generalist robots. The purpose of this document is to share the excitement …

This paper describes a deep reinforcement learning (DRL) approach that won Phase 1 of
the Real Robot Challenge (RRC) 2021, and then extends this method to a more difficult …

Manipulation tasks can often be decomposed into multiple subtasks performed in parallel,
eg, sliding an object to a goal pose while maintaining contact with a table. Individual …

We present RobotGPT, an innovative decision framework for robotic manipulation that
prioritizes stability and safety. The execution code generated by ChatGPT cannot guarantee …

We present ArrayBot, a distributed manipulation system consisting of a $16\times 16$ array
of vertically sliding pillars integrated with tactile sensors, which can simultaneously support …

In policy learning for robotic manipulation, sample efficiency is of paramount importance.
Thus, learning and extracting more compact representations from camera observations is a …

In this work, we propose algorithms and methods that enable learning dexterous object
manipulation using simulated one-or two-armed robots equipped with multi-fingered hand …

This work re-implements the OpenAI Gym multi-goal robotic manipulation environment,
originally based on the commercial Mujoco engine, onto the open-source Pybullet engine …

Humans excel in complex long-horizon soft body manipulation tasks via flexible tool use:
bread baking requires a knife to slice the dough and a rolling pin to flatten it. Often regarded …

Robotic manipulation in complex open-world scenarios requires both reliable physical
manipulation skills and effective and generalizable perception. In this paper, we propose …