Advances in artificial intelligence are stimulating interest in neuroscience. However, most
attention is given to discrete tasks with simple action spaces, such as board games and …

Inverse kinematics (IK) is the use of kinematic equations to determine the joint parameters of
a manipulator so that the end effector moves to a desired position; IK can be applied in many …

Animals are capable of precise and agile locomotion using vision. Replicating this ability
has been a long-standing goal in robotics. The traditional approach has been to decompose …

Reinforcement learning (RL) is typically viewed as the problem of estimating single-step
policies (for model-free RL) or single-step models (for model-based RL), leveraging the …

The incredible feats of athleticism demonstrated by humans are made possible in part by a
vast repertoire of general-purpose motor skills, acquired through years of practice and …

Legged robots that can operate autonomously in remote and hazardous environments will
greatly increase opportunities for exploration into underexplored areas. Exteroceptive …

Synthesizing graceful and life-like behaviors for physically simulated characters has been a
fundamental challenge in computer animation. Data-driven methods that leverage motion …

We introduce a method for generating realistic pedestrian trajectories and full-body
animations that can be controlled to meet user-defined goals. We draw on recent advances …

Learning the spatial-temporal structure of body movements is a fundamental problem for
character motion synthesis. In this work, we propose a novel neural network architecture …

Developing robust walking controllers for bipedal robots is a challenging endeavor.
Traditional model-based locomotion controllers require simplifying assumptions and careful …