Zucker Dataset: Human-robot
interaction dataset recorded at Clemson's Zucker Family Graduate Education Center in May 2022.
CNav: Python code for
synthesizing composite motions for physically-based characters using reinforcement learning, generative modeling, and incremental learning.
CNav: C++ code for distributed coordination in multi-agent navigation settings
SocialVAE:
Python library for state-of-the-art agent trajectory prediction that relies on social attention and a timewise variational autoencoder
Implicit Crowds: C++ library for robust and guaranteed collision-free crowd simulation that relies on optimization-based implicit integration
ICCGAN: Python code for interactive control of physically simulated characters using reinforcement learning and Generative Adversarial Networks
KDMA: Python code for human-inspired multi-agent navigation using reinforcement learning and knowledge distillation
NH-TTC: C++library for fast, anticipatory steering of mobile robots having arbitrary equations of motions
PFPN:
General deep RL framework for particle-based exploration of high-dimensional action spaces during training of physics-based character controllers
PowerLaw:
C++and Python code for human-like collision avoidance between agents elaborated from analyzing a large corpus of pedestrian trajectory data