Dibyendu Das

Projects

• Self Evaluation, Robot evauating its confidence in performing complex manipulation tasks [code] Paper: [arXiv,2024] Self-evaluation is a probabilistic algorithm that enables the Robot to evaluate whether, for a particular task to be performed in a given workspace, it has a sufficient number of demonstrations in order to generate feasible manipulation plans while satisfying the task-related motion constraints. The underlying method relies upon an \(\epsilon\)-optimal arm identification in a Multi-armed Bandit setup using \((\epsilon,\delta)\)-PAC (Probably Approximately Correct ) learning.
• UnreLyzer, A static interval analyzer for C like unreliable programs [code] Paper: [arXiv,2016] Unrelyzer is a static analysis tool that analyzes a program defined by a subset of C (Mini-C) grammar with an addendum where each arithmetic, boolean and memory (Read/Write) operation in the program is probabilistic/unreliable in nature. This tool statically analyzes the program using Abstract Interpretation in the interval domain and determines the interval of values with a certain confidence for each program variable at each program point. This tool was built as part of my Master’s thesis.
• ProPFA, Probabilistic Path based Failure Analyzer [code] Paper: [SETTA,2016] This automatic tool uses a path based failure analysis approach to estimate failure probability of C programs. It is designed as an integrated framework that takes as input a C program annotated with failure assertions, the discrete ranges and probability density functions of all independent input variables. It estimates success probability of each execution path of a program separately and returns failure probability of the whole program along with a measure of confidence on the paths explored within a defined time and memory bound.

Publications

• Screw Geometry Meets Bandits: Incremental Acquisition of Demonstrations to Generate Manipulation Plans, in the Journal of IEEE Robotics and Automation Letters (RAL). Dibyendu Das, Aditya Patankar, Nilanjan Chakraborty, C.R. Ramakrishnan, and I.V. Ramakrishnan

• Knowledge-Enabled Motion Generation for Complex Manipulation Tasks, in IEEE ICRA Workshop on Geometric Representations: The Roles of Modern Screw Theory, Lie Algebra, and Geometric Algebra in Robotics, London, UK, 2023. Dibyendu Das, Aditya Patankar, Fumi Honda, Dasharadhan Mahalingam, Nilanjan Chakraborty, C.R. Ramakrishnan, and I.V. Ramakrishnan

• Select or Suggest? Reinforcement Learning-based Method for High-Accuracy Target Selection on Touchscreens, in Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, pp. 1-15. Zhi Li, Maozheng Zhao, Dibyendu Das, Hang Zhao, Yan Ma, Wanyu Liu, Michel Beaudouin-Lafon, Fusheng Wang, IV Ramakrishnan, and Xiaojun Bi

• Work In Progress: Verifying Stability Guarantees of Control Software Implementations in the Presence of Sensor Level Faults, in Proceedings of the 13th ACM International Conference on Embedded Software (EMSOFT). Saurav Kumar Ghosh, Debasmita Lohar, Dibyendu Das, and Soumyajit Dey

• Failure Estimation of Behavioral Specifications, in International Symposium on Dependable Software Engineering: Theories, Tools, and Applications (SETTA), pp. 315-322, Springer International Publishing. [code] Debasmita Lohar, Anudeep Dunaboyina, Dibyendu Das, and Soumyajit Dey