I am a Ph.D. student in robot learning advised by Prof. Jan Peters at the Intelligent Autonomous Systems group at TU Darmstadt. My research interests include robot learning, (inverse) reinforcement learning, imitation learning, and decision-making under partial observability. Currently, my research aims to improve the state of robotic locomotion and manipulation through data-driven reward functions, reinforcement learning, and visual/language inputs.

I received my MSc. in Robotics with cum laude honours from TU Delft where I worked on generative imitation learning techniques under the supervision of Prof. Jens Kober. In the past, I interned in multi-agent robotics at Bosch Research and held positions in graph-based ML at the Wadhwani School of Data Science & AI at IIT Madras.

Email: anish.diwan [at] robot-learning [dot] de
            anish.diwan [at] tu-darmstadt [dot] de

Socials: LinkedIn | GitHub | Notes

news

Apr 01, 2025	I started my Ph.D. in robot learning at IAS, TU Darmstadt!
Jan 22, 2025	My MSc. thesis was accepted at ICLR 2025
Sep 19, 2024	I graduated MSc. Robotics from TU Delft with cum laude! 🎓

publications

1. 

   Noise-conditioned Energy-based Annealed Rewards (NEAR): A Generative Framework for Imitation Learning from Observation

   Anish Abhijit Diwan, Julen Urain, Jens Kober, and Jan Peters

   In International Conference on Learning Representations (ICLR), 2025

   Abs arXiv Website

   This paper introduces a new imitation learning framework based on energy-based generative models capable of learning complex, physics-dependent, robot motion policies through state-only expert motion trajectories. Our algorithm, called Noise-conditioned Energy-based Annealed Rewards (NEAR), constructs several perturbed versions of the expert’s motion data distribution and learns smooth, and well-defined representations of the data distribution’s energy function using denoising score matching. We propose to use these learnt energy functions as reward functions to learn imitation policies via reinforcement learning. We also present a strategy to gradually switch between the learnt energy functions, ensuring that the learnt rewards are always well-defined in the manifold of policy-generated samples. We evaluate our algorithm on complex humanoid tasks such as locomotion and martial arts and compare it with state-only adversarial imitation learning algorithms like Adversarial Motion Priors (AMP). Our framework sidesteps the optimisation challenges of adversarial imitation learning techniques and produces results comparable to AMP in several quantitative metrics across multiple imitation settings.