Control Barrier Functions for Safety-Aware Latent-Space Motion Planning
Overview
This research proposes replacing binary collision classifiers with neural control barrier functions (CBFs) in latent-space path planning for robotic manipulators. By training a CBF with safe/unsafe sign constraints and discrete-time decrease conditions, the method enforces safety via first-order corrections during planning. On a 7-DoF Franka Panda, it achieves a 75.6% success rate on fixed-obstacle tasks (12.0 percentage points above the best of 35 classifier configurations) and 83.8% on multi-position tasks. A decay rate sweep identifies optimal safety-performance trade-offs in α ∈ [0.10, 0.20], though random-obstacle settings yield a 33.0% success rate due to covariate shift between obstacle-blind training and corrected planner states.
Generated from attributed reports · Updated 2 hours ago
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Reported quantity · decay rate alpha range: 0.1 other · Basis not reported
“characterizes the safety--performance trade-off, with the best operating points in $\alpha \in [0.10, 0.20]$. In a fully general random-obstacle setting, success drops to 33.0\%, which we trace to covariate shift between the obstacle-blind training transitions and the states visited by the corrected planner.”
Exact source · revision 1Source owner not reported
Reported quantity · decay rate alpha range upper bound: 0.2 other · Basis not reported
“characterizes the safety--performance trade-off, with the best operating points in $\alpha \in [0.10, 0.20]$. In a fully general random-obstacle setting, success drops to 33.0\%, which we trace to covariate shift between the obstacle-blind training transitions and the states visited by the corrected planner.”
Exact source · revision 1Source owner not reported
Reported quantity · robot degrees of freedom: 7 other · Basis not reported
“planner, and it is enforced at planning time by a closed-form, first-order correction that projects each goal-directed latent step back onto the CBF constraint. On a 7-DoF Franka Panda manipulator, the CBF planner achieves a 75.6\% success rate on a fixed-obstacle task, 12.0 percentage points above the best of 35 tuned classifier configurations, and 83.8\% on a multi-position task. A sweep of the decay rate $\alpha$”
Exact source · revision 1Source owner not reported
Report timeline
Follow attributed reports and material updates.
- arXiv Robotics — research abstractsSignalControl Barrier Functions for Safety-Aware Latent-Space Motion Planning
This research proposes replacing binary collision classifiers with neural control barrier functions (CBFs) in latent-space path planning for robotic manipulators. By training a CBF with safe/unsafe sign constraints and discrete-time decrease conditions, the method enforces safety via first-order corrections during planning. On a 7-DoF Franka Panda, it achieves a 75.6% success rate on fixed-obstacle tasks (12.0 percentage points above the best of 35 classifier configurations) and 83.8% on multi-position tasks. A decay rate sweep identifies optimal safety-performance trade-offs in α ∈ [0.10, 0.20], though random-obstacle settings yield a 33.0% success rate due to covariate shift between obstacle-blind training and corrected planner states.
Event coverage history
There is not enough continuous observation data to show a trend.
Timezone · UTC
Article dates follow your selected timezone. Briefing editions use Hong Kong time (UTC+8).