539 citations · 644 across the 12 of their papers we have counts for
19 papers
Probabilistic Modeling for Human Mesh Recovery
Nikos Kolotouros, Georgios Pavlakos, Dinesh Jayaraman +1
This paper focuses on the problem of 3D human reconstruction from 2D evidence. Although this is an inherently ambiguous problem, the majority of recent works avoid the uncertainty…
Conservative Offline Distributional Reinforcement Learning
Yecheng Jason Ma, Dinesh Jayaraman, Osbert Bastani
Many reinforcement learning (RL) problems in practice are offline, learning purely from observational data. A key challenge is how to ensure the learned policy is safe, which requi…
Keyframe-Focused Visual Imitation Learning
Chuan Wen, Jierui Lin, Jianing Qian +2
Imitation learning trains control policies by mimicking pre-recorded expert demonstrations. In partially observable settings, imitation policies must rely on observation histories,…
How Are Learned Perception-Based Controllers Impacted by the Limits of Robust Control?
Jingxi Xu, Bruce Lee, Nikolai Matni +1
The difficulty of optimal control problems has classically been characterized in terms of system properties such as minimum eigenvalues of controllability/observability gramians. W…
Likelihood-Based Diverse Sampling for Trajectory Forecasting
Yecheng Jason Ma, Jeevana Priya Inala, Dinesh Jayaraman +1
Forecasting complex vehicle and pedestrian multi-modal distributions requires powerful probabilistic approaches. Normalizing flows (NF) have recently emerged as an attractive tool…
Fighting Copycat Agents in Behavioral Cloning from Observation Histories
Chuan Wen, Jierui Lin, Trevor Darrell +2
Imitation learning trains policies to map from input observations to the actions that an expert would choose. In this setting, distribution shift frequently exacerbates the effect…