41 citations · 59 across the 5 of their papers we have counts for
7 papers
Barkour: Benchmarking Animal-level Agility with Quadruped Robots
Ken Caluwaerts, Atil Iscen, J. Chase Kew +41
Animals have evolved various agile locomotion strategies, such as sprinting, leaping, and jumping. There is a growing interest in developing legged robots that move like their biol…
Learning and Adapting Agile Locomotion Skills by Transferring Experience
Laura Smith, J. Chase Kew, Tianyu Li +5
Legged robots have enormous potential in their range of capabilities, from navigating unstructured terrains to high-speed running. However, designing robust controllers for highly…
Legged Robots that Keep on Learning: Fine-Tuning Locomotion Policies in the Real World
Laura Smith, J. Chase Kew, Xue Bin Peng +3
Legged robots are physically capable of traversing a wide range of challenging environments, but designing controllers that are sufficiently robust to handle this diversity has bee…
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…
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…
Long-Range Indoor Navigation with PRM-RL
Anthony Francis, Aleksandra Faust, Hao-Tien Lewis Chiang +4
Long-range indoor navigation requires guiding robots with noisy sensors and controls through cluttered environments along paths that span a variety of buildings. We achieve this wi…
FollowNet: Robot Navigation by Following Natural Language Directions with Deep Reinforcement Learning
Pararth Shah, Marek Fiser, Aleksandra Faust +2
Understanding and following directions provided by humans can enable robots to navigate effectively in unknown situations. We present FollowNet, an end-to-end differentiable neural…