4 accepted papers
The ability to interactively learn skills from human guidance and adjust behavior according to human preference is crucial to accelerating robot learning. But human guidance is an expensive resource, calling for methods that can learn efficiently. In this work, we argue that learning is more efficie…
We present BEHAVIOR-1K, a comprehensive simulation benchmark for human-centered robotics. BEHAVIOR-1K includes two components, guided and motivated by the results of an extensive survey on "what do you want robots to do for you?". The first is the definition of 1,000 everyday activities, grounded in…
Robots operating in everyday environments need to effectively perceive; model; and infer semantic properties of objects. Existing knowledge reasoning frameworks only model binary relations between an object's class label and its semantic properties; unable to collectively reason about object propert…
We address the problem of adapting robot trajectories to improve safety, comfort, and efficiency in humanrobot collaborative tasks. To this end, we propose CoMOTO, a trajectory optimization framework that utilizes stochastic motion prediction to anticipate the human's motion and adapt the robot's jo…