publications
publications and preprints in reversed chronological order.
* indicates equal contribution.
2026
- Angchen Xie*, Nikhil Sobanbabu*, Ishayu Shikhare, Alan Wang, Max Simchowitz, and Guanya ShiIn Conference on Robot Learning (CoRL), 2026CoRL 2026RSS 2026 Sim2Real Workshop
High-precision humanoid control is limited by target-domain dynamics mismatch, where the same control objective can induce different realized motions under changes in terrain, payload, or actuator response. Existing methods either pursue zero-shot transfer through domain randomization or in-context adaptation without target-domain specialization, or require heavy adaptation pipelines that leverage target-domain data, such as model calibration, residual learning, or policy retraining. We present FADA (Few-Shot Domain Adaptation via Dynamics Alignment), a three-stage Planner-Inverse Dynamics Model (Planner-IDM) framework for few-shot adaptation in humanoid control. FADA first trains an oracle policy with privileged information and then distills the oracle behavior into a deployable Planner-IDM student through DAgger. At deployment, FADA freezes the planner and finetunes only the IDM using approximately 2 minutes of target-domain rollouts with standard supervised learning. Rather than requiring optimal demonstrations or rewards, FADA uses the paired actions and observations observed during these rollouts as supervision, aligning the IDM’s action generation with target-domain dynamics. Experiments show that FADA outperforms both in-context and end-to-end adaptation baselines, improving task performance under dynamics shifts and enabling real humanoid robots to execute diverse high-precision whole-body tasks.
@inproceedings{fada2026, author = {Xie, Angchen and Sobanbabu, Nikhil and Shikhare, Ishayu and Wang, Alan and Simchowitz, Max and Shi, Guanya}, booktitle = {Conference on Robot Learning (CoRL)}, title = {FADA: Few-Shot Domain Adaptation via Dynamics Alignment for Humanoid Control}, year = {2026}, note = {CoRL 2026}, archiveprefix = {arXiv}, primaryclass = {cs.RO}, url = {https://arxiv.org/abs/2606.28476}, urldate = {2026-07-06}, }
2025
- Zhikai Zhang, Siqi Guo, Henry Kou, Ishayu Shikhare, Howie Choset, and Lu LiIn 2025 IEEE International Conference on Robotics and Automation (ICRA), 2025ICRA 2025
With relatively fewer neurons than more complex life forms, insects are still capable of producing astonishing locomotive behaviors, such as traversing diverse habitats and making rapid gait adaptations after extreme injury or autotomy. Biologists attribute this to a chain of segmental neuron clusters (ganglia) within insect nervous systems, which act as distributed, self-organizing sensorimotor control units. Inspired by the neural structure of the Carausius morosus, the common stick insect, this research introduces the Distributed Neural Locomotion Controller (D-NLC), a modular control framework utilizing local proprioceptive feedback to modulate joint-level Central Pattern Generator (CPG) signals to produce emergent locomotive behaviors. We implemented this framework using a modular legged robot with distributed joint-level embedded computing units and assessed its performance and behavior under various experimental settings. Based on real-world experiments, we observe an overall 31.3% average increase in curvilinear motion performance under external (terrain) and internal (amputation) disturbances compared to a centralized predefined gait controller. This difference is statistically significant (P << 0.05) for larger perturbations but not for single-leg amputations. Experiments with perturbation-induced leg stance duration and leg-phase-difference analysis further validated our hypothesis regarding D-NLC’s role in the robust perceptive locomotion and self-emergent gait adaptation against complex unforeseen perturbations. This proposed control framework does not require any numerical optimization or weight training processes, which are time-consuming and computationally expensive. To the best of our knowledge, this framework is the first bio-inspired neural controller deployed on a distributed embedded system.
@inproceedings{11128090, author = {Zhang, Zhikai and Guo, Siqi and Kou, Henry and Shikhare, Ishayu and Choset, Howie and Li, Lu}, booktitle = {2025 IEEE International Conference on Robotics and Automation (ICRA)}, title = {Bio-Inspired Distributed Neural Locomotion Controller (D-NLC) for Robust Locomotion and Emergent Behaviors}, year = {2025}, volume = {}, number = {}, pages = {12303-12309}, keywords = {Legged locomotion;Training;Perturbation methods;Insects;Neurons;Propioception;Process control;Amputation;Robustness;Optimization}, note = {ICRA 2025}, url = {https://eigenbot-dnlc.github.io/}, doi = {10.1109/ICRA55743.2025.11128090}, urldate = {2026-01-28}, }