Arjun Bhardwaj — accepted AI-conference papers

2026

ViserDex: Visual Sim-to-Real for Robust Dexterous In-hand Reorientation

Arjun Bhardwaj, Maximum Wilder-Smith, Mayank Mittal, Vaishakh Patil, Marco Hutter

RSS 2026poster

In-hand object reorientation requires precise estimation of the object pose to handle complex task dynamics. While RGB sensing offers rich semantic cues for pose tracking, existing solutions rely on multi-camera setups or costly ray tracing. We present a sim-to-real framework for monocular RGB in-ha…

Cited by 0 Source ↗Scholar ↗

2024

Data-Efficient Task Generalization via Probabilistic Model-Based Meta Reinforcement Learning

Arjun Bhardwaj, Jonas Rothfuss, Bhavya Sukhija, Yarden As, Marco Hutter, Stelian Coros +1

RA-L 2024

We introduce PACOH-RL, a novel model-based Meta-Reinforcement Learning (Meta-RL) algorithm designed to efficiently adapt control policies to changing dynamics. PACOH-RL meta-learns priors for the dynamics model, allowing swift adaptation to new dynamics with minimal interaction data. Existing Meta-R

Cited by 10 Source ↗Scholar ↗