59 citations · 221 across the 105 of their papers we have counts for
8 papers · 1 filter
Model-Free Adaptive Parameter Tuning for Efficient Multi-Robot Warehouse Operations
Pratap Tokekar, Mouhacine Benosman, Rahul Chandan +3
Robotic Fulfillment Centers (FCs) store inventory on shelves (pods) arranged in dense blocks. Retrieving a target pod that is buried deep in a block requires moving obstructing pod…
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…
DeltaRubric: Generative Multimodal Reward Modeling via Joint Planning and Verification
Rui Liu, Dian Yu, Zhenwen Liang +6
Aligning Multimodal Large Language Models (MLLMs) requires reliable reward models, yet existing single-step evaluators can suffer from lazy judging, exploiting language priors over…
Reinforcing Multimodal Reasoning Against Visual Degradation
Rui Liu, Dian Yu, Haolin Liu +6
Reinforcement Learning has significantly advanced the reasoning capabilities of Multimodal Large Language Models (MLLMs), yet the resulting policies remain brittle against real-wor…
Zero Shot Coordination for Sparse Reward Tasks with Diverse Reward Shapings
Keenan Powell, Peihong Yu, Pratap Tokekar
Many Multi-Agent Reinforcement Learning (MARL) agents fail to adapt properly to cooperating with agents trained with the same objectives but different seeds, algorithms, or other t…
WESPR: Wind-adaptive Energy-Efficient Safe Perception & Planning for Robust Flight with Quadrotors
Khuzema Habib, Pranav Deshakulkarni Manjunath, Kasra Torshizi +2
Local wind conditions strongly influence drone performance: headwinds increase flight time, crosswinds and wind shear hinder agility in cluttered spaces, while tailwinds reduce tra…
Active Asymmetric Multi-Agent Multimodal Learning under Uncertainty
Rui Liu, Pratap Tokekar, Ming Lin
Multi-agent systems are increasingly equipped with heterogeneous multimodal sensors, enabling richer perception but introducing modality-specific and agent-dependent uncertainty. E…
PRISM: Performer RS-IMLE for Single-pass Multisensory Imitation Learning
Amisha Bhaskar, Pratap Tokekar, Stefano Di Cairano +1
Robotic imitation learning typically requires models that capture multimodal action distributions while operating at real-time control rates and accommodating multiple sensing moda…