25 citations · 51 across the 47 of their papers we have counts for
8 papers · 1 filter
Adaptive Human Trajectory Prediction via Latent Corridors
Neerja Thakkar, Karttikeya Mangalam, Andrea Bajcsy +1
Human trajectory prediction is typically posed as a zero-shot generalization problem: a predictor is learnt on a dataset of human motion in training scenes, and then deployed on un…
Deception Game: Closing the Safety-Learning Loop in Interactive Robot Autonomy
Haimin Hu, Zixu Zhang, Kensuke Nakamura +2
An outstanding challenge for the widespread deployment of robotic systems like autonomous vehicles is ensuring safe interaction with humans without sacrificing performance. Existin…
What Matters to You? Towards Visual Representation Alignment for Robot Learning
Ran Tian, Chenfeng Xu, Masayoshi Tomizuka +2
When operating in service of people, robots need to optimize rewards aligned with end-user preferences. Since robots will rely on raw perceptual inputs like RGB images, their rewar…
Conformal Decision Theory: Safe Autonomous Decisions from Imperfect Predictions
Jordan Lekeufack, Anastasios N. Angelopoulos, Andrea Bajcsy +2
We introduce Conformal Decision Theory, a framework for producing safe autonomous decisions despite imperfect machine learning predictions. Examples of such decisions are ubiquitou…
Learning Vision-based Pursuit-Evasion Robot Policies
Andrea Bajcsy, Antonio Loquercio, Ashish Kumar +1
Learning strategic robot behavior -- like that required in pursuit-evasion interactions -- under real-world constraints is extremely challenging. It requires exploiting the dynamic…
StROL: Stabilized and Robust Online Learning from Humans
Shaunak A. Mehta, Forrest Meng, Andrea Bajcsy +1
Robots often need to learn the human's reward function online, during the current interaction. This real-time learning requires fast but approximate learning rules: when the human'…
Contingency Games for Multi-Agent Interaction
Lasse Peters, Andrea Bajcsy, Chih-Yuan Chiu +4
Contingency planning, wherein an agent generates a set of possible plans conditioned on the outcome of an uncertain event, is an increasingly popular way for robots to act under un…
Towards Modeling and Influencing the Dynamics of Human Learning
Ran Tian, Masayoshi Tomizuka, Anca Dragan +1
Humans have internal models of robots (like their physical capabilities), the world (like what will happen next), and their tasks (like a preferred goal). However, human internal m…