Shared autonomy

Overview

Shared autonomy means the human and the robot both contribute to the motion the robot executes. The human keeps some control and the robot adds help, either continuously or over part of the task. The invariant the variants share is that one command reaches the actuators, built from two sources, and a rule decides how much of it comes from the robot 2402.02758 §3.2 [arxiv:2411.01014 §2.7].

The variants differ in how that rule is set. Pan et al. fix one robot weight for a whole trial and measure how it changes workload and trust. Penco et al. do not blend throughout. The robot recognises the task from the operator's first motion, adapts a learned motion to it, and hands the grasp to a stored sequence through a short transition. The operator then accepts or rejects a preview and moves the plan with a joystick 2411.01014 §2.3 §2.7 §3.

Two papers with cards cover the topic. Their tasks, robots and measures differ, so their numbers cannot be ranked against each other.

Variants

Fixed blend weight

The human target and the robot target are combined as a convex mix. The robot has weight gamma and the human has 1 minus gamma. Gamma of 1 is full autonomy and gamma of 0 is none 2402.02758 §3.2 2402.02758 eq. 3. The robot target is a precomputed trajectory, and the human target is the handle position scaled by a constant. The main phase had four gamma values, but only 0.2 and 0.8 were analysed 2402.02758 §3.5.1 2402.02758 §4. The paper does not explain why the other two were left out. Card: Effects of Shared Control on Cognitive Load and Trust in Teleoperated Trajectory Tracking.

Results, author-reported, within subject with 24 people. Self-reported workload fell and trust rose with more robot share 2402.02758 tab. 1 2402.02758 §4.2 §4.3. Tracking error fell from 2.16 cm to 0.83 cm between the low and high levels 2402.02758 tab. 2. The dual-task tapping measures and the pupil measure showed no clear effect 2402.02758 §4.2.

Learned plan with affordance takeover

The robot holds one learned probabilistic movement primitive (ProMP) per task. Each was learned from fewer than 30 demonstrations recorded in simulation. It picks the task whose primitive is closest to the operator's first motion, then conditions that primitive on the motion and on the object pose [arxiv:2411.01014 §2] §2.3 §2.4. Where an Affordance Template exists for the object, a logistic weight blends from the primitive into the template's first sample. Where none exists, the primitive runs alone 2411.01014 §2.7 eq. 9 eq. 10 §4.1. The blend's stated end conditions cannot both hold for this logistic form with finite parameters, so an implementation has to pick values and accept a compromise 2411.01014 §2.7 eq. 10. Card: Mixed Reality Teleoperation Assistance for Direct Control of Humanoids.

Results, author-reported. In an eight-person door-opening study, assisted control completed all trials. Direct control completed 75.0% for experts and 58.3% for novices, and assisted runs took about half the time [arxiv:2411.01014 tab. 2] §4.2. The study ran no statistical tests. The punching evaluation gave a left-hand RMS difference of 2.35 cm, but it compared the adapted primitive with the same user's trajectory, with no baseline 2411.01014 tab. 1 §4.1.

When to use which

Evidence strength differs. The blend study has 24 participants and reports tests. The learned plan has 8 participants and no tests, so its speed and completion gains are not established by the paper. Do not combine their numbers.

Implementation sketch

The fixed blend is one line per control step. The takeover needs a task-selection step and a blend into a stored sequence. Constants in the second block are not stated in the paper and must be chosen.

# Fixed blend, once per control step  [arxiv:2402.02758 eq. 3]
# Input:  gamma in [0, 1] (robot weight); robot_target, human_target (3,)
# Output: commanded end-effector position (3,)
def blend(gamma, robot_target, human_target):
    # gamma = 1 is full autonomy, gamma = 0 is none  [arxiv:2402.02758 §3.2]
    return gamma * robot_target + (1 - gamma) * human_target

# Learned plan with takeover  [arxiv:2411.01014 §2.3-§2.7]
# Input:  primitives (one per task); first_motion; object_pose; template
# Output: reference trajectory
def assist(primitives, first_motion, object_pose, template):
    # Pick the task whose primitive best matches the early motion  [§2.3]
    task = closest_primitive(primitives, first_motion)
    # Condition on the observed motion and the object pose  [§2.4]
    plan = condition(task, first_motion, object_pose)
    if template is None:
        # No affordance: the primitive runs alone  [§4.1]
        return plan
    # Logistic weight; a, b, c are not stated in the paper  [arxiv:2411.01014 eq. 10]
    # Mix primitive samples into the template's first sample  [arxiv:2411.01014 eq. 9]
    return blend_into(plan, template.first_sample)

Contradictions

None known between the two papers. They study different tasks and report different measures 2402.02758 §3 2411.01014 §4.

Within one paper, the self-reported workload result in the blend study did not appear in the dual-task and pupil measures 2402.02758 §4.2.

Papers

• Effects of Shared Control on Cognitive Load and Trust in Teleoperated Trajectory Tracking: a fixed robot-weight blend, tested in a 24-person study of workload and trust.
• Mixed Reality Teleoperation Assistance for Direct Control of Humanoids: learned movement primitives with an affordance-template takeover on a humanoid, tested with eight people.