Learning contactfrom motor current

In free space, a camera can show where an object is and joint positions can show where the arm is. Contact-rich manipulation is harder because the decisive state may be physical rather than purely geometric. A gripper can close without securing an object, a peg can reach a hole while pressing against its rim, and two nearly identical camera images can precede either a smooth insertion or a jam.

Low-cost bus servos offer another possibility. The SO-101 used in this study is fitted with Waveshare ST3215 servos, whose control table specifies a read-only motor-current register. Reading that existing register adds no sensor or mechanical modification to the study arm.

Visual occlusion

This loss of direct visual evidence near contact is a central observability problem in contact-rich imitation learning.

1. 1

   Images and joint positions provide strong information about appearance, configuration, and motion. They do not directly measure interaction effort.

2. 2

   Contact-aware policies therefore augment vision and kinematics with tactile, force, or torque measurements. These modalities can reveal contact onset, load, deformation, or slip, but they usually require additional hardware, calibration, or a robot that already provides reliable torque estimates.

3. 3

   The SO-101 used in this study is fitted with Waveshare ST3215 servos, whose control table specifies a read-only motor-current register. Reading that existing register adds no sensor or mechanical modification to the study arm.

4. 4

   Its interpretation is nevertheless difficult because motor-side current is affected by pose, motion, friction, and the high-ratio transmission, and it does not directly specify the direction or location of an external force.

Three questions make the investigation operational. These questions separate three claims that could otherwise be confused: that the signal contains task-relevant information, that a trained model uses that information, and that its use improves closed-loop behaviour.

Characterisation of the current signal

Before motor current can be added to a policy, it must first be shown to carry a repeatable and task-relevant signal.

Dataset

A fair comparison between a vision-based policy and a current-aware policy requires more than recording both modalities. The policies must learn from the same demonstrations, images, joint positions, and action targets, while the additional current input is introduced through a reproducible transformation.

Evaluation

Binary task success is the primary endpoint. A trial succeeds when the peg is fully seated and flush with the fixture surface before the timeout; every other outcome is a failure. One operator resets and scores every trial.

Results

Under the evaluated data, preprocessing, fusion, and execution settings, adding current does not produce a reliable improvement in closed-loop insertion success.

The unchanged ACT-V checkpoint varies significantly across sessions, and both Diffusion Policy arms deteriorate together across blocks. These patterns show why a contemporaneous control is essential and why absolute success counts from separate days cannot be used to rank current representations. Current is informative at the sensor level and can influence predicted actions, but the tested policies do not convert that information into a repeatable behavioural advantage.

Conclusion

The evidence supports a clear distinction between the information available in the motor-current signal and the control benefit achieved by the evaluated policies.

Citation

@mastersthesis{ranpariya2026contact,
  author = {Jaydeepkumar Ranpariya},
  title  = {Learning Contact Representations from Zero-Cost
            Proprioceptive Effort Signals for Contact-Rich
            Visuomotor Imitation Learning},
  school = {Friedrich-Alexander-Universit{\"a}t Erlangen-N{\"u}rnberg},
  year   = {2026},
  note   = {In cooperation with Fraunhofer IIS},
  url    = {https://contact-from-current.pages.dev/}
}