6 citations · 16 across the 4 of their papers we have counts for
8 papers
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…
Model-based Reinforcement Learning for Decentralized Multiagent Rendezvous
Rose E. Wang, J. Chase Kew, Dennis Lee +5
Collaboration requires agents to align their goals on the fly. Underlying the human ability to align goals with other agents is their ability to predict the intentions of others an…
Learned Critical Probabilistic Roadmaps for Robotic Motion Planning
Brian Ichter, Edward Schmerling, Tsang-Wei Edward Lee +1
Sampling-based motion planning techniques have emerged as an efficient algorithmic paradigm for solving complex motion planning problems. These approaches use a set of probing samp…
Neural Collision Clearance Estimator for Batched Motion Planning
J. Chase Kew, Brian Ichter, Maryam Bandari +2
We present a neural network collision checking heuristic, ClearanceNet, and a planning algorithm, CN-RRT. ClearanceNet learns to predict separation distance (minimum distance betwe…
Zero-shot Imitation Learning from Demonstrations for Legged Robot Visual Navigation
Xinlei Pan, Tingnan Zhang, Brian Ichter +3
Imitation learning is a popular approach for training visual navigation policies. However, collecting expert demonstrations for legged robots is challenging as these robots can be…
Robot Motion Planning in Learned Latent Spaces
Brian Ichter, Marco Pavone
This paper presents Latent Sampling-based Motion Planning (L-SBMP), a methodology towards computing motion plans for complex robotic systems by learning a plannable latent represen…