Research:
The Effect of Incorporating Motor Current Feedback in an Imitation Learning Policy for a Multi-DOF Robot

Traditional imitation learning for contact-rich manipulation usually uses images and joint positions. This project tests whether gripper motor current, a low-cost proxy for force, when added to an ACT policy improves performance in a task sorting identical soft-vs-hard cubes.

hypothesis + task

If motor current feedback is included as an input, task accuracy should improve. The robot picks a cube from a fixed point and places it left for soft TPU and right for hard PLA.

materials

  • 2 SO-101 arms (leader + follower)
  • Logitech C920x webcam
  • LeRobot by Hugging Face
  • Google Colab for training
  • 1.5 inch TPU and PLA cubes

model + data collection

The model uses Action Chunking Transformer (ACT), which predicts action chunks to reduce compounding error. This version appends gripper current to image and joint-state inputs.

Data came from teleoperated 15-second demonstrations at 30 Hz, recording webcam frames, 6-DOF joint states, and gripper current. Total training set: 56 recordings.

Robot setup with leader and follower arms

training + inference

Baseline and current-augmented models were trained in Colab (about 7 hours each on NVIDIA A100). For baseline control, motor current values were replaced with zeros.

At inference time, the policy generates 100-step chunks every 100 timesteps. Chunk generation is about 0.176s and chunk execution about 3.33s at 30 Hz.

ACT model architecture with gripper current input

training + inference gifs

PLA

PLA training run
PLA training
PLA inference run
PLA inference

TPU

TPU training run
TPU training
TPU inference run
TPU inference

results

Motor current feedback improved total accuracy from 77.5% to 95%, supporting the hypothesis that current adds useful contact information for distinguishing soft vs hard objects.

ModelTPUPLASuccessesFailuresAccuracy
Baseline ACT15/2016/2031977.5%
Current-Augmented ACT20/2019/2039195%
Chart comparing TPU vs PLA gripper current
Gripper current feedback for TPU vs PLA cubes
Chart comparing follower and leader motor positions for TPU vs PLA
Follower and leader motor positions for TPU vs PLA

conclusions

Motor current provided a strong complementary signal when visual appearance was similar and joint differences were subtle. This improved confidence and task success in low-cost hardware.

further research

  • Test visually distinct objects to isolate multimodal effects.
  • Include current from all 6 joints for richer contact feedback.
  • Validate on practical tasks like fruit picking.

references

  • Zhao TZ, et al. (2023). "Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware." arXiv. Available from: https://arxiv.org/abs/2304.13705
  • Tsuji T, et al. (2025). "A Survey on Imitation Learning for Contact-Rich Tasks in Robotics." arXiv. Available from: https://arxiv.org/abs/2506.13498
  • He Z, et al. (2024). "FoAR: Force-Aware Reactive Policy for Contact-Rich Robotic Manipulation." arXiv. Available from: https://arxiv.org/abs/2411.15753
  • Li Y, Hannaford B. (2017). "Gaussian Process Regression for Sensorless Grip Force Estimation of Cable Driven Elongated Surgical Instruments." IEEE Robotics and Automation Letters, 2(3):1312–1319.
  • Kobayashi M, et al. (2024). "ILBiT: Imitation Learning for Robot Using Position and Torque Information based on Bilateral Control with Transformer." arXiv. Available from: https://arxiv.org/abs/2401.16653

full poster (pdf)