Designed a transformer-based actor-critic architecture with a novel tokenization scheme to train a single reinforcement-learning policy across multiple robot morphologies simultaneously. Conditioned the policy on BERT-encoded URDF (robot-structure) descriptions, enabling generalization to robot embodiments unseen during training, without per-robot retraining.
Optimized a semantic-segmentation model for real-time scene understanding on an autonomous-vehicle platform, targeting the NVIDIA Jetson ORIN edge device. Converted the PyTorch model to an INT8-quantized TensorRT engine (calibrated on ~6,000 images), achieving a 3× inference speedup (from ~50 to ~150 fps) while maintaining an acceptable accuracy threshold.
Developed and implemented an actor-critic algorithm to solve constrained optimization for cooperative multi-agent reinforcement learning. Evaluated the approach on two benchmark multi-agent environments — Cooperative Navigation and Cooperative Treasure Collection — using an Azure cloud virtual machine. This work led to a publication at AAMAS 2021.
Implemented research papers for customer-specific use cases across reinforcement learning, NLP and computer vision. Selected projects include Explainable Product Recommendation, Airline Seat Inventory and Dynamic Pricing, and Fine-Grained Visual Classification.
Deployment of imitation-learning policies (ACT, SmolVLA, π0.5) on a SO-101 robotic arm: collected teleoperated demonstrations and fine-tuned policies that execute manipulation tasks on the arm. Full evaluation, quantified results, and failure analysis on the Robot Learning page.
Built an offline RL pipeline (TD3+BC) for vision-based manipulation on the WidowX arm in simulation, on the pick-and-place experimental setup from the COG paper. Replaced the CNN visual encoder with a pretrained ResNet-18 backbone, achieving faster and more stable convergence than a CQL baseline within 200 epochs. (code)
Fine-tuned a Vision Transformer with LaCLIP embeddings on the MMSD 2.0 (image–text pairs) dataset, taking advantage of text-augmented pre-training and to test whether the richer text supervision improves cross-modal reasoning. (code)
AAMAS 2021 (Proceedings of the 20th International Conference on Autonomous Agents and Multiagent Systems), 2021
P. Parnika*, Raghuram Bharadwaj Diddigi*, Sai Koti Reddy Danda* and Shalabh Bhatnagar. "Attention Actor-Critic Algorithm for Multi-Agent Constrained Co-operative Reinforcement Learning." AAMAS 2021.
(* denotes equal contribution)
Paper Code
M.Sc. research report, Université de Montréal — REAL Lab, Mila (advisor: Prof. Glen Berseth), 2025
A transformer-based actor-critic architecture with a novel tokenization scheme that trains a single reinforcement-learning policy across multiple robot morphologies, conditioned on BERT-encoded URDF descriptions to generalize to embodiments unseen during training.
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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