11 papers
Learning Communication-Conditioned Generative Policies for Decentralized Multi-Agent Collision Avoidance
Prajwal Koirala, Mark Campbell
In this work, we propose a decentralized communication-conditioned generative framework for multi-agent collision avoidance. Agents generate short-horizon action sequences using a…
VGFM: Expressive Robot Policies via Dense Value Guidance in Flow Matching
Prajwal Koirala, Mark Campbell
Recent robot learning paradigms increasingly rely on large offline datasets of robotic interactions to train control policies. Expressive generative models enable rich and multimod…
LexiSafe: Offline Safe Reinforcement Learning with Lexicographic Safety-Reward Hierarchy
Hsin-Jung Yang, Zhanhong Jiang, Prajwal Koirala +3
Offline safe reinforcement learning (RL) is increasingly important for cyber-physical systems (CPS), where safety violations during training are unacceptable and only pre-collected…
LCLA: Language-Conditioned Latent Alignment for Vision-Language Navigation
Nitesh Subedi, Adam Haroon, Samuel Tetteh +3
We propose LCLA (Language-Conditioned Latent Alignment), a framework for vision-language navigation that learns modular perception-action interfaces by aligning sensory observation…
Neural CDEs as Correctors for Learned Time Series Models
Muhammad Bilal Shahid, Zhanhong Jiang, Prajwal Koirala +2
Learned time-series models, whether continuous or discrete, are widely used for forecasting the states of dynamical systems but suffer from error accumulation in multi-step forecas…
Can Pretrained Vision-Language Embeddings Alone Guide Robot Navigation?
Nitesh Subedi, Adam Haroon, Shreyan Ganguly +4
Foundation models have revolutionized robotics by providing rich semantic representations without task-specific training. While many approaches integrate pretrained vision-language…
Flow-Based Single-Step Completion for Efficient and Expressive Policy Learning
Prajwal Koirala, Cody Fleming
Generative models such as diffusion and flow-matching offer expressive policies for offline reinforcement learning (RL) by capturing rich, multimodal action distributions, but thei…
FAWAC: Feasibility Informed Advantage Weighted Regression for Persistent Safety in Offline Reinforcement Learning
Prajwal Koirala, Zhanhong Jiang, Soumik Sarkar +1
Safe offline reinforcement learning aims to learn policies that maximize cumulative rewards while adhering to safety constraints, using only offline data for training. A key challe…
Latent Safety-Constrained Policy Approach for Safe Offline Reinforcement Learning
Prajwal Koirala, Zhanhong Jiang, Soumik Sarkar +1
In safe offline reinforcement learning (RL), the objective is to develop a policy that maximizes cumulative rewards while strictly adhering to safety constraints, utilizing only of…
F1tenth Autonomous Racing With Offline Reinforcement Learning Methods
Prajwal Koirala, Cody Fleming
Autonomous racing serves as a critical platform for evaluating automated driving systems and enhancing vehicle mobility intelligence. This work investigates offline reinforcement l…
Solving Offline Reinforcement Learning with Decision Tree Regression
Prajwal Koirala, Cody Fleming
This study presents a novel approach to addressing offline reinforcement learning (RL) problems by reframing them as regression tasks that can be effectively solved using Decision…