Ajinkya Jain

Hi there! I am a robotics researcher at Intrinsic.ai (an Alphabet company), where I am a part of the R&D team led by Prof. Stefan Schaal. My research lies at the intersection of artificial intelligence (AI) and machine learning (ML) with robot motion planning, dynamics, and control. I am particularly interested in developing methods that enable robots to perform dexterous manipulation tasks with robustness in the face of uncertainty.

Before joining Intrinsic, I obtained my Ph.D. in robotics from the University of Texas at Austin under the guidance of Prof. Scott Niekum and Prof. Ashish Deshpande . My doctoral research focused on two key areas: developing learning algorithms for robots to acquire object interaction models from visual data and motion planners to perform robot manipulation tasks robustly under uncertainty. Prior to joining UT Austin, I obtained my Bachelors and Masters in Mechanical Engineering from IIT Kanpur.

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I'm interested in robot learning, dexterous robot manipulation, motion planning under uncertainty, model learning for planning and control, reinforcement learning, and optimal control.

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Open X-Embodiment: Robotic Learning Datasets and RT-X Models

Open X-Embodiment Collaboration, Ajinkya Jain, et al.
IEEE International Conference on Robotics and Automation (ICRA), 2024
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Large-scale datasets in standardized data formats and models to enable learning “generalist” X-robot policy that can be adapted efficiently to new robots, tasks, and environments.

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Efficient Online Learning of Contact Force Models for Connector Insertion

Kevin Tracy, Zachary Manchester, Ajinkya Jain, Keegan Go, Stefan Schaal, Tom Erez, and Yuval Tassa
ArXiv Dec, 2024
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An efficient and scalable approach for modeling connector insertion environments by learning a quasi-static contact force model instead of a full simulator, employing a Linear Model Learning algorithm for real-time optimal mapping without matrix inversions.

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Distributional Depth-Based Estimation of Object Articulation Models

Ajinkya Jain, Stephen Giguere, Rudolf Lioutikov, Scott Niekum
5th Conference on Robot Learning (CoRL), 2021
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A novel method that efficiently learns distributions over articulation model parameters directly from depth images without the need to know articulation model categories a priori

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ScrewNet: Category-Independent Articulation Model Estimation From Depth Images Using Screw Theory

Ajinkya Jain, Rudolf Lioutikov, Caleb Chuck, Scott Niekum
IEEE International Conference on Robotics and Automation (ICRA), 2021
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A novel method for estimating articulation models for objects directly from raw depth images without knowing their articulation type a priori using screw theory

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Learning Hybrid Object Kinematics for Efficient Hierarchical Planning Under Uncertainty

Ajinkya Jain, Scott Niekum
IEEE/ RSJ International Conference on Intelligent Robots and Systems (IROS), 2020
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A method for learning planning-compatible hybrid kinematics models for articulated objectsfrom human demonstrations

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Efficient Hierarchical Robot Motion Planning Under Uncertainty and Hybrid Dynamics

Ajinkya Jain, Scott Niekum
Conference on Robot Learning (CoRL), 2018
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A POMDP-based Motion planner that generates efficient motion plans leveraging object-object interactions to perform manipulation tasks under uncertainty with high success rates

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Belief Space Planning under Approximate Hybrid Dynamics

Ajinkya Jain, Scott Niekum
Workshop on POMDPs in Robotics, Robotics: Science and Systems (RSS), 2017
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Extending Belief-space LQR to hybrid systems for robot motion planning under uncertainty

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A piezoelectric model based multi-objective optimization of robot gripper design

Rituparna Datta, Ajinkya Jain, Bishakh Bhattacharya
Structural and Multidisciplinary Optimization, Springer, 2015
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Multi-objective design optimization of a piezoelectric actuator driven gripper

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Unified Minimalistic Modelling of Piezoelectric Stack Actuators for Engineering Applications

Ajinkya Jain,
Advances in Intelligent Systems and Computing, Springer, 2014
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A simple, computationally-cheap, yet effective model for piezoelectric stack actuators as a replacement of black-box models used in engineering design optimization problems.

Design and source code from Jon Barron's website. Jekyll version from Leonid Keselman's website