Pratap Tokekar

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…

cs.CL2026

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…

cs.CV2026

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…

cs.LG2026

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…

cs.LG2026

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…

Stable and Efficient Single-Rollout RL for Multimodal Reasoning

Rui Liu, Dian Yu, Lei Ke +6

Reinforcement Learning with Verifiable Rewards (RLVR) has become a key paradigm to improve the reasoning capabilities of Multimodal Large Language Models (MLLMs). However, prevalen…

Dual-Uncertainty Guided Policy Learning for Multimodal Reasoning

Rui Liu, Dian Yu, Tong Zheng +8

Reinforcement learning with verifiable rewards (RLVR) has advanced reasoning capabilities in multimodal large language models. However, existing methods typically treat visual inpu…

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…

Contextual Neural Moving Horizon Estimation for Robust Quadrotor Control in Varying Conditions

Kasra Torshizi, Chak Lam Shek, Khuzema Habib +3

Adaptive controllers on quadrotors typically rely on estimation of disturbances to ensure robust trajectory tracking. Estimating disturbances across diverse environmental contexts…

MMCD: Multi-Modal Collaborative Decision-Making for Connected Autonomy with Knowledge Distillation

Rui Liu, Zikang Wang, Peng Gao +3

Autonomous systems have advanced significantly, but challenges persist in accident-prone environments where robust decision-making is crucial. A single vehicle's limited sensor ran…

GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning

Kelin Yu, Sheng Zhang, Harshit Soora +4

Recent advances have shown that video generation models can enhance robot learning by deriving effective robot actions through inverse dynamics. However, these methods heavily depe…

Multi-Agent Trust Region Policy Optimisation: A Joint Constraint Approach

Chak Lam Shek, Guangyao Shi, Pratap Tokekar

Multi-agent reinforcement learning (MARL) requires coordinated and stable policy updates among interacting agents. Heterogeneous-Agent Trust Region Policy Optimization (HATRPO) enf…

Learning Multi-Robot Coordination through Locality-Based Factorized Multi-Agent Actor-Critic Algorithm

Chak Lam Shek, Amrit Singh Bedi, Anjon Basak +5

In this work, we present a novel cooperative multi-agent reinforcement learning method called \textbf{Loc}ality based \textbf{Fac}torized \textbf{M}ulti-Agent \textbf{A}ctor-\textb…

Option Discovery Using LLM-guided Semantic Hierarchical Reinforcement Learning

Chak Lam Shek, Pratap Tokekar

Large Language Models (LLMs) have shown remarkable promise in reasoning and decision-making, yet their integration with Reinforcement Learning (RL) for complex robotic tasks remain…

PEnGUiN: Partially Equivariant Graph NeUral Networks for Sample Efficient MARL

Joshua McClellan, Greyson Brothers, Furong Huang +1

Equivariant Graph Neural Networks (EGNNs) have emerged as a promising approach in Multi-Agent Reinforcement Learning (MARL), leveraging symmetry guarantees to greatly improve sampl…

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…

CAML: Collaborative Auxiliary Modality Learning for Multi-Agent Systems

Rui Liu, Yu Shen, Peng Gao +2

Multi-modal learning has emerged as a key technique for improving performance across domains such as autonomous driving, robotics, and reasoning. However, in certain scenarios, par…