3 papers
cs.LG2026
Q-based Variational Inverse Reinforcement Learning
Ondrej Bajgar, Peter Tisnikar, Alessandro Abate +2
The development of safe and beneficial AI requires that systems can learn and act in accordance with human preferences. However, explicitly specifying these preferences by hand is…
cs.AI2026
Partner Capability Estimation for Task-Agnostic Adaptation in Ad-Hoc Teamwork
Peter Tisnikar, Maja Swieczkowska, Benteng Ma +2
Effective collaboration with novel and diverse partners is a crucial skill for autonomous agents. Most current ad-hoc teamwork (AHT) approaches assume that agents will collaborate…
cs.RO2023
When and Where to Step: Terrain-Aware Real-Time Footstep Location and Timing Optimization for Bipedal Robots
Ke Wang, Zhaoyang Jacopo Hu, Peter Tisnikar +3
Online footstep planning is essential for bipedal walking robots, allowing them to walk in the presence of disturbances and sensory noise. Most of the literature on the topic has f…