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A Safety-Aware Shared Autonomy Framework with BarrierIK Using Control Barrier Functions

Berk Guler, Kay Pompetzki, Yuanzheng Sun, Simon Manschitz, Jan Peters

TL;DR

This work investigates the use of control barrier functions at the inverse kinematics layer of shared autonomy, targeting post-blend safety while preserving task performance in cluttered environments and in a VR teleoperation study comparing pure teleoperation with shared autonomy.

Abstract

Shared autonomy blends operator intent with autonomous assistance. In cluttered environments, linear blending can produce unsafe commands even when each source is individually collision-free. Many existing approaches model obstacle avoidance through potentials or cost terms, which only enforce safety as a soft constraint. In contrast, safety-critical control requires hard guarantees. We investigate the use of control barrier functions (CBFs) at the inverse kinematics (IK) layer of shared autonomy, targeting post-blend safety while preserving task performance. Our approach is evaluated in simulation on representative cluttered environments and in a VR teleoperation study comparing pure teleoperation with shared autonomy. Across conditions, employing CBFs at the IK layer reduces violation time and increases minimum clearance while maintaining task performance. In the user study, participants reported higher perceived safety and trust, lower interference, and an overall preference for shared autonomy with our safety filter. Additional materials available at https://berkguler.github.io/barrierik.

A Safety-Aware Shared Autonomy Framework with BarrierIK Using Control Barrier Functions

TL;DR

This work investigates the use of control barrier functions at the inverse kinematics layer of shared autonomy, targeting post-blend safety while preserving task performance in cluttered environments and in a VR teleoperation study comparing pure teleoperation with shared autonomy.

Abstract

Shared autonomy blends operator intent with autonomous assistance. In cluttered environments, linear blending can produce unsafe commands even when each source is individually collision-free. Many existing approaches model obstacle avoidance through potentials or cost terms, which only enforce safety as a soft constraint. In contrast, safety-critical control requires hard guarantees. We investigate the use of control barrier functions (CBFs) at the inverse kinematics (IK) layer of shared autonomy, targeting post-blend safety while preserving task performance. Our approach is evaluated in simulation on representative cluttered environments and in a VR teleoperation study comparing pure teleoperation with shared autonomy. Across conditions, employing CBFs at the IK layer reduces violation time and increases minimum clearance while maintaining task performance. In the user study, participants reported higher perceived safety and trust, lower interference, and an overall preference for shared autonomy with our safety filter. Additional materials available at https://berkguler.github.io/barrierik.
Paper Structure (6 sections, 5 equations, 5 figures, 1 table)

This paper contains 6 sections, 5 equations, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. A Safety Aware Shared Autonomy Framework
  4. Experiments
  5. Results
  6. Conclusion and Future Work

Figures (5)

  • Figure 1: Pipeline with CBF-based safety filtering after arbitration. The operator specifies a target pose $\mathbf{T}_{\mathrm{h}}(t)$; the guiding policy outputs $\mathbf{T}_{\mathrm{r}}(t)$ from the current state $\boldsymbol{s}(t)~=~\{\boldsymbol{\theta}(t), \boldsymbol{\mathcal{O}}(t), \boldsymbol{\mathcal{L}}(t), \text{task context}\}$, where $\boldsymbol{\theta}$ is the joint configuration, $\boldsymbol{\mathcal{O}}$ and $\boldsymbol{\mathcal{L}}$ are obstacle and robot link capsules, and "task context" includes task-specific info (e.g., rendered views). These unfiltered poses are blended in $SE(3)$ via arbitration weight $\alpha(t)$ to form $\mathbf{T}(t)$ (red dashed), which may violate safety margins. BarrierIK receives $\mathbf{T}(t)$ and enforces CBF conditions to compute a safe joint command $\boldsymbol{\theta}^{*}(t)$ (green), which is applied to the robot. The updated state $\boldsymbol{s}(t)$ is fed back to the policy and user.
  • Figure 2: Simulated task and collision-evaluation setup. Left: scene with human target $\mathbf{T}_\text{h}(t)$ and autonomous reference $\mathbf{T}_\text{r}(t)$. Right: robot link (red) and obstacle (black) convex-capsule colliders; for each link $\ell\in\mathcal{L}$ and obstacle $o\in\mathcal{O}$ we compute signed distances $\phi_{\ell o}(\bm{\theta})$ and signed distance vectors used by Baseline P and BarrierIK (colors encode magnitude).
  • Figure 3: The stacked bar chart illustrates the number of participants who ranked each controller configuration from 1st to 6th choice. Each color segment within a bar represents the count of users selecting that configuration at the respective rank.
  • Figure 4: Subjective workload and usability across teleoperation configurations. (a) Polar (“radar”) plot of modified NASA-TLX dimensions—physical demand (I-PD), temporal demand (I-TD), mental demand (I-MD), performance (I-PER), effort (I-EFF), frustration (I-FL), control level (CL), assistance level (AL), and safety level (SL)—with “I-” denoting inverted scales so that larger values correspond to more favorable assessments. (b) Violin plots of participants' aggregated NASA-TLX scores for each configuration (mean $\circ$, median $\blacklozenge$), where lower workload (higher support) appears toward the top of the scale.
  • Figure 5: Objective evaluation across six system configurations. Subplots (a)–(c) report performance and safety metrics: success rate (higher is better), task completion time (lower is better, measured only for successful trials), number of collisions (lower is better). $\blacklozenge$ indicates the median, asterisks indicate statistical significance: $^*$$p<.05$, $^{**}$$p<.01$, $^{***}$$p<.001$.