269 citations · 2k across the 61 of their papers we have counts for
8 papers · 1 filter
Wish you were here: Hindsight Goal Selection for long-horizon dexterous manipulation
Todor Davchev, Oleg Sushkov, Jean-Baptiste Regli +4
Complex sequential tasks in continuous-control settings often require agents to successfully traverse a set of "narrow passages" in their state space. Solving such tasks with a spa…
From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence
Nicholas Roy, Ingmar Posner, Tim Barfoot +17
Machine learning has long since become a keystone technology, accelerating science and applications in a broad range of domains. Consequently, the notion of applying learning metho…
Offline Meta-Reinforcement Learning for Industrial Insertion
Tony Z. Zhao, Jianlan Luo, Oleg Sushkov +5
Reinforcement learning (RL) can in principle let robots automatically adapt to new tasks, but current RL methods require a large number of trials to accomplish this. In this paper,…
Multi-Task Learning with Sequence-Conditioned Transporter Networks
Michael H. Lim, Andy Zeng, Brian Ichter +5
Enabling robots to solve multiple manipulation tasks has a wide range of industrial applications. While learning-based approaches enjoy flexibility and generalizability, scaling th…
CaTGrasp: Learning Category-Level Task-Relevant Grasping in Clutter from Simulation
Bowen Wen, Wenzhao Lian, Kostas Bekris +1
Task-relevant grasping is critical for industrial assembly, where downstream manipulation tasks constrain the set of valid grasps. Learning how to perform this task, however, is ch…
A Robustness Analysis of Inverse Optimal Control of Bipedal Walking
John R. Rebula, Stefan Schaal, James Finley +1
Cost functions have the potential to provide compact and understandable generalizations of motion. The goal of Inverse Optimal Control (IOC) is to analyze an observed behavior whic…
Benchmarking Off-The-Shelf Solutions to Robotic Assembly Tasks
Wenzhao Lian, Tim Kelch, Dirk Holz +2
In recent years, many learning based approaches have been studied to realize robotic manipulation and assembly tasks, often including vision and force/tactile feedback. However, it…
Robust Multi-Modal Policies for Industrial Assembly via Reinforcement Learning and Demonstrations: A Large-Scale Study
Jianlan Luo, Oleg Sushkov, Rugile Pevceviciute +6
Over the past several years there has been a considerable research investment into learning-based approaches to industrial assembly, but despite significant progress these techniqu…