1 citations · 1 across the 5 of their papers we have counts for
5 papers
STeP: Signal Temporal Logic for Precise Specifications for Action Generation with Vision Language Models
Kasra Torshizi, Anukriti Singh, Sidharth Mathur +3
Natural-language robot instructions often specify more than a coarse task goal: they may impose spatial, temporal, and logical requirements that must remain satisfied throughout ex…
AFFORD2ACT: How Far Can Sparse Keypoints Go for Robot Manipulation?
Anukriti Singh, Kasra Torshizi, Khuzema Habib +3
Vision-based manipulation policies are commonly learned from rich visual observations, typically using dense visual features extracted from full resolution images. It remains uncle…
VARP: Reinforcement Learning from Vision-Language Model Feedback with Agent Regularized Preferences
Anukriti Singh, Amisha Bhaskar, Peihong Yu +4
Designing reward functions for continuous-control robotics often leads to subtle misalignments or reward hacking, especially in complex tasks. Preference-based RL mitigates some of…
Sketch-to-Skill: Bootstrapping Robot Learning with Human Drawn Trajectory Sketches
Peihong Yu, Amisha Bhaskar, Anukriti Singh +2
Training robotic manipulation policies traditionally requires numerous demonstrations and/or environmental rollouts. While recent Imitation Learning (IL) and Reinforcement Learning…
REBEL: Reward Regularization-Based Approach for Robotic Reinforcement Learning from Human Feedback
Souradip Chakraborty, Anukriti Singh, Amisha Bhaskar +3
The effectiveness of reinforcement learning (RL) agents in continuous control robotics tasks is mainly dependent on the design of the underlying reward function, which is highly pr…