publications

2025

1. Not All Collisions are Bad: Adapting to User Contact Preferences for Whole-Arm Manipulation

   Madan, Rishabh, Lin, Jiawei, Goel, Mahika, Li, Amber, Xie, Angchen, Liang, Xiaoyu, Lee, Marcus, Guo, Justin, Thakkar, Pranav N., Banerjee, Rohan, Barreiros, Jose A., Tsui, Katherine M., and Bhattacharjee, Tapomayukh

   In Under Review, ACM/IEEE International Conference on Human-Robot Interaction (HRI) 2025

2024

1. 

   CushSense: Soft, Stretchable, and Comfortable Tactile-Sensing Skin for Physical Human-Robot Interaction

   Xu, Boxin, Zhong, Luoyan, Zhang, Grace, Liang, Xiaoyu, Virtue, Diego, Madan, Rishabh, and Bhattacharjee, Tapomayukh

   In IEEE International Conference on Robotics and Automation (ICRA) 2024

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2. 

   RABBIT: A robot-assisted bed bathing system with multimodal perception and integrated compliance

   Madan, Rishabh*, Valdez, Skyler*, Kim, David, Fang, Sujie, Zhong, Luoyan, Virtue, Diego T., and Bhattacharjee, Tapomayukh

   In ACM/IEEE International Conference on Human-Robot Interaction (HRI) 2024

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2022

1. 

   SPARCS: Structuring Physically Assistive Robotics for Caregiving with Stakeholders-in-the-loop

   Madan, Rishabh*, Jenamani, Rajat Kumar*, Nguyen, Vy Thuy, Moustafa, Ahmed, Hu, Xuefeng, Dimitropoulou, Katherine, and Bhattacharjee, Tapomayukh

   In IEEE International Conference on Intelligent Robots and Systems (IROS) 2022

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2021

1. 

   A Robot Cluster for Reproducible Research in Dexterous Manipulation

   In Preprint, Under Review 2021

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2. 

   Benchmarking Structured Policies and Policy Optimization for Real-World Dexterous Object Manipulation

   Funk, Niklas*, Schaff, Charles*, Madan, Rishabh*, Yoneda, Takuma*, De Jesus, Julen Urain, Watson, Joe, Gordon, Ethan K, Widmaier, Felix, Bauer, Stefan, Srinivasa, Siddhartha S, Bhattacharjee, Tapomayukh, Walter, Matthew, and Peters, Jan

   In IEEE Robotics and Automation Letters (RA-L) 2021

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2020

1. 

   Multimodal Trajectory Prediction via Topological Invariance for Navigation at Uncontrolled Street Intersections

   Roh, Junha*, Mavrogiannis, Christoforos*, Madan, Rishabh*, Fox, Dieter, and Srinivasa, Siddhartha

   In Conference on Robot Learning (CoRL) 2020

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2. 

   ExTra: Transfer-guided Exploration

   Santara, Anirban*, Madan, Rishabh*, Mitra, Pabitra, and Ravindran, Balaraman

   In Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems 2020

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   In this work we present a novel approach for transfer-guided exploration in reinforcement learning that is inspired by the human tendency to leverage experiences from similar encounters in the past while navigating a new task. Given an optimal policy in a related task-environment, we show that its bisimulation distance from the current task-environment gives a lower bound on the optimal advantage of state-action pairs in the current task-environment. Transfer-guided Exploration (ExTra) samples actions from a Softmax distribution over these lower bounds. In this way, actions with potentially higher optimum advantage are sampled more frequently. In our experiments on gridworld environments, we demonstrate that given access to an optimal policy in a related task-environment, ExTra can outperform popular domain-specific exploration strategies viz. epsilon greedy, Model-Based Interval Estimation – Exploration Bonus (MBIE-EB), Pursuit and Boltzmann in rate of convergence. We further show that ExTra is robust to choices of source task and shows a graceful degradation of performance as the dissimilarity of the source task increases. We also demonstrate that ExTra, when used alongside traditional exploration algorithms, improves their rate of convergence. Thus it is capable of complementing the efficacy of traditional exploration algorithms.

2019

1. 

   Traffic Sign Classification using Hybrid HOG-SURF Features and Convolutional Neural Networks

   Madan, Rishabh*, Agrawal, D. C.*, Kowshik, Shreyas*, Maheshwari, Harsh*, Agarwal, Siddhant*, and Chakravarty, Debashish

   In ICPRAM 2019

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   Traffic signs play an important role in safety of drivers and regulation of traffic. Traffic sign classification is thus an important problem to solve for the advent of autonomous vehicles. There have been several works that focus on traffic sign classification using various machine learning techniques. While works involving the use of convolutional neural networks with RGB images have shown remarkable results, they require a large amount of training time, and some of these models occupy a huge chunk of memory. Earlier works like HOG-SVM make use of local feature descriptors for classification problem but at the expense of reduced performance. This paper explores the use of hybrid features by combining HOG features and SURF with CNN classifier for traffic sign classification. We propose a unique branching based CNN classifier which achieves an accuracy of 98.48% on GTSRB test set using just 1.5M trainable parameters.