Author

Mayank Mittal

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Preprint Nov 2026

GoalDex: Learning Image-Goal-Conditioned Policies for Dexterous In-Hand Manipulation

Dexterous in-hand manipulation is commonly formulated around explicit object pose estimation, yet reliable pose tracking is challenging under severe hand-object occlusions and often requires object-specific supervision. This dependence creates a bottleneck between perception and control that can limit robustness and sc...

Arjun Bhardwaj, Mayank Mittal, Maximum Wilder-Smith et al. · 0 citations

#artificial intelligence Preprint Sep 2026

ForceTwin: Physics-informed Digital Twins for Robotic Manipulation from Instrumented Human Interaction

ForceTwin is a system for identifying physics-informed digital twins of articulated objects from instrumented human interaction that achieves 87% goal completion across nine object-embodiment pairs, compared with 60% using VLM-prior and 57% using kinematics-only twins.

Tim Engelbracht, René Zurbrügg, Mayank Mittal et al. · 1 citation

Open access Aug 2026

Contact-Guided Exploration for Non-Prehensile Locomanipulation With Multi-Critic RL

This work proposes a contact-guided exploration strategy implemented within a Multi-Critic Reinforcement Learning (RL) framework, and obtains candidate interaction points from a general-purpose grasping algorithm, enabling the exploration mechanism to generalise across various object geometries.

Simone Tolomei, Mayank Mittal, F. Angelini et al. · IEEE Robotics and Automation... · 0 citations