Maks Sorokin

Learning Loco-Manipulation From SMPC Demonstrations With Sparse Offline-to-Online RL

Martin Schuck, Maks Sorokin, Simone Manni, Duy Ta, Angela P. Schoellig, Marco Hutter, Simon Le Cleac'h, Jan Brüdigam

Conference on Robot Learning (CoRL) 2026

selected for a spotlight (top 3.4% of 2,094 submissions)

Sample-based MPC in simulation acts as an automated expert whose offline data solves exploration, so off-policy RL learns loco-manipulation from sparse rewards alone. The policies surpass their optimal-control teacher and transfer to Spot and a Unitree G1 humanoid.

SUMO: Dynamic and Generalizable Whole-Body Loco-Manipulation

John Z. Zhang, Maks Sorokin*, Jan Brüdigam*, Brandon Hung*, Stephen Phillips, Dmitry Yershov, Farzad Niroui, Tong Zhao, Leonor Fermoselle, Xinghao Zhu, Chao Cao, Duy Ta, Tao Pang, Jiuguang Wang, Preston Culbertson, Zachary Manchester, Simon Le Cléac'h

IEEE Transactions on Robot Learning (T-RL) 2026

A sim-to-real approach that enables legged robots to dynamically manipulate large and heavy objects with whole-body dexterity. We combine pre-trained whole-body policies with sample-based planning in a hierarchical controller that generalizes to diverse objects and tasks without retraining — demonstrated on Spot uprighting a 15kg tire and dragging barriers larger than itself, as well as humanoid simulations.

AdaptManip: Learning Adaptive Whole-Body Object Lifting and Delivery with Online Recurrent State Estimation

Morgan Byrd, Donghoon Baek, Kartik Garg, Hyunyoung Jung, Daesol Cho, Maks Sorokin, Robert Wright, Sehoon Ha

Under review (RA-L), 2026

An autonomous framework that enables humanoid robots to navigate, grasp, lift, and deliver objects with whole-body coordination. A recurrent pose estimator maintains robust state under noisy vision, and the policy generalizes across varying object shapes and masses with adaptive regrasping.

Jacta: A Versatile Planner for Learning Dexterous and Whole-body Manipulation

Jan Brüdigam, Ali Adeeb Abbas, Maks Sorokin, Kuan Fang, Brandon Hung, Maya Guru, Stefan Sosnowski, Jiuguang Wang, Sandra Hirche, Simon Le Cleac'h

IEEE Robotics and Automation Letters (RA-L) 2024

We combine reinforcement learning with sampling-based planning to solve contact-rich manipulation tasks. Sampling-based planners quickly find successful trajectories, but the resulting solutions often lack robustness. We use RL to distill a set of planner demonstrations into a single policy that handles real-world variation and uncertainty.

On Designing a Learning Robot: Improving Morphology for Enhanced Task Performance and Learning

Maks Sorokin, Chuyuan Fu, Jie Tan, C. Karen Liu, Yunfei Bai, Wenlong Lu, Sehoon Ha, Mohi Khansari

International Conference on Intelligent Robots and Systems (IROS) 2023

A learning-oriented morphology optimization framework that jointly accounts for a robot's body, its onboard perception, and how the two interact across tasks. Holistically optimized morphologies improve task performance by 15–20% across a range of manipulation problems, and require 25× less data to match a human-expert-designed morphology.

Relax, it doesn't matter how you get there!

Mehdi Azabou, Michael Mendelson, Maks Sorokin, Shantanu Thakoor, Nauman Ahad, Carolina Urzay, Eva L Dyer

Neural Information Processing Systems (NeurIPS) 2023

selected for a spotlight

We introduce Bootstrap Across Multiple Scales (BAMS), a multi-scale self-supervised representation-learning model for behavior analysis. BAMS pools features from encoders with different temporal receptive fields and applies latent bootstrapping objectives in each scale's embedding space, encouraging disentanglement across timescales.

Learning to Navigate Sidewalks in Outdoor Environments

Maks Sorokin, Jie Tan, C. Karen Liu, Sehoon Ha

IEEE Robotics and Automation Letters (RA-L) 2022

A system that enables zero-shot transfer of vision-based policies to outdoor sidewalk navigation. Evaluated on a quadrupedal robot walking 3.2 km of real-world sidewalks with minimal human intervention.

Human Motion Control of Quadrupedal Robots using Deep Reinforcement Learning

Sunwoo Kim, Maks Sorokin, Jehee Lee, Sehoon Ha

Proceedings of Robotics: Science and Systems (RSS) 2022

A motion-control system that lets a human user drive a quadrupedal robot through a wide range of motor tasks — standing, sitting, tilting, manipulating, walking, and turning — in both simulation and on hardware.

Learning Human Search Behavior from Egocentric View

Maks Sorokin, Wenhao Yu, Sehoon Ha, C. Karen Liu

EUROGRAPHICS 2021

We train a vision-based agent to perform object search in a photorealistic 3D scene, and propose a motion-synthesis mechanism that retargets the agent's head motion onto an animated human character (PFNN/NSM) — transferring its search behavior to a humanoid avatar.

A Few Shot Adaptation of Visual Navigation Skills to New Observations using Meta-Learning

Qian Luo, Maks Sorokin, Sehoon Ha

The IEEE International Conference on Robotics and Automation (ICRA) 2021

Vision-based navigation agents can be trained to adapt to new sensor configurations with as few as three shots of experience. Rapid adaptation comes from a bottleneck between the perception and control networks, plus meta-adaptation of the perception component.