# Neural Configuration-Space Barriers for Manipulation Planning and Control

arXiv: https://arxiv.org/abs/2503.04929 · HTML: https://mlsanity.com/paper/2503.04929/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 67 references that are in the map.
- First posted on arXiv 6 Mar 2025.

## Builds on (most cited first, 30 of 35)

- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation](https://mlsanity.com/paper/1901.05103/) (2019)
- [Implicit Neural Representations with Periodic Activation Functions](https://mlsanity.com/paper/2006.09661/) (2020)
- [Control Barrier Functions: Theory and Applications](https://mlsanity.com/paper/1903.11199/) (2019)
- [Data-driven Distributionally Robust Optimization Using the Wasserstein Metric: Performance Guarantees and Tractable Reformulations](https://mlsanity.com/paper/1505.05116/) (2015)
- [Control Barrier Function Based Quadratic Programs for Safety Critical Systems](https://mlsanity.com/paper/1609.06408/) (2016)
- [Voxblox: Incremental 3D Euclidean Signed Distance Fields for On-Board MAV Planning](https://mlsanity.com/paper/1611.03631/) (2016)
- [FIESTA: Fast Incremental Euclidean Distance Fields for Online Motion Planning of Aerial Robots](https://mlsanity.com/paper/1903.02144/) (2019)
- [Learning Models as Functionals of Signed-Distance Fields for Manipulation Planning](https://mlsanity.com/paper/2110.00792/) (2021)
- [GCBF+: A Neural Graph Control Barrier Function Framework for Distributed Safe Multi-Agent Control](https://mlsanity.com/paper/2401.14554/) (2024)
- [Learning Barrier Functions with Memory for Robust Safe Navigation](https://mlsanity.com/paper/2011.01899/) (2020)
- [Semantically Safe Robot Manipulation: From Semantic Scene Understanding to Motion Safeguards](https://mlsanity.com/paper/2410.15185/) (2024)
- [Representing Robot Geometry as Distance Fields: Applications to Whole-body Manipulation](https://mlsanity.com/paper/2307.00533/) (2023)
- [Configuration Space Distance Fields for Manipulation Planning](https://mlsanity.com/paper/2406.01137/) (2024)
- [Robust Control Barrier Functions under High Relative Degree and Input Constraints for Satellite Trajectories](https://mlsanity.com/paper/2107.04094/) (2021)
- [Safety-Critical Manipulation for Collision-Free Food Preparation](https://mlsanity.com/paper/2205.01026/) (2022)
- [Certified Polyhedral Decompositions of Collision-Free Configuration Space](https://mlsanity.com/paper/2302.12219/) (2023)
- [Configuration Space Decomposition for Scalable Proxy Collision Checking in Robot Planning and Control](https://mlsanity.com/paper/2201.04314/) (2022)
- [Efficient Motion Planning for Manipulators with Control Barrier Function-Induced Neural Controller](https://mlsanity.com/paper/2404.01184/) (2024)
- [Sensor-Based Distributionally Robust Control for Safe Robot Navigation in Dynamic Environments](https://mlsanity.com/paper/2405.18251/) (2024)
- [RAMP: Hierarchical Reactive Motion Planning for Manipulation Tasks Using Implicit Signed Distance Functions](https://mlsanity.com/paper/2305.10534/) (2023)
- [Stochastic Implicit Neural Signed Distance Functions for Safe Motion Planning under Sensing Uncertainty](https://mlsanity.com/paper/2309.16862/) (2023)
- [Safe Planning for Articulated Robots Using Reachability-based Obstacle Avoidance With Spheres](https://mlsanity.com/paper/2402.08857/) (2024)
- [Safe Bubble Cover for Motion Planning on Distance Fields](https://mlsanity.com/paper/2408.13377/) (2024)
- [Faster Algorithms for Growing Collision-Free Convex Polytopes in Robot Configuration Space](https://mlsanity.com/paper/2410.12649/) (2024)
- [Feasibility Analysis and Regularity Characterization of Distributionally Robust Safe Stabilizing Controllers](https://mlsanity.com/paper/2311.05813/) (2023)
- [Multi-Modal MPPI and Active Inference for Reactive Task and Motion Planning](https://mlsanity.com/paper/2312.02328/) (2023)
- [Efficient Collision Detection Framework for Enhancing Collision-Free Robot Motion](https://mlsanity.com/paper/2409.14955/) (2024)
- [Fast and Safe Path-Following Control using a State-Dependent Directional Metric](https://mlsanity.com/paper/2002.02038/) (2020)

## Built on by (most cited first, 3 of 3)

- [X-Safe: Embodiment-Agnostic Safeguarding for Robot Arm Manipulation Policies via Action Masking](https://mlsanity.com/paper/2606.22278/) (2026)
- [VLPSA: Vision-Language-Poisson-Safe Actions for Full-Body Safety of Learned Policies](https://mlsanity.com/paper/2609.22462/) (2026)
- [CrossSafe: Towards Cross-Embodiment Latent Safety Filters](https://mlsanity.com/paper/2609.28984/) (2026)

## Abstract (arXiv)

Planning and control for high-dimensional robot manipulators in cluttered dynamic environments require computational efficiency and robust safety guarantees. Inspired by recent advances in learning configuration-space distance functions (CDFs) as representations of robot bodies, we propose a unified approach for motion planning and control that formulates safety constraints as CDF barriers. A CDF barrier approximates the local free configuration space, substantially reducing the number of collision-checking operations during motion planning. However, learning a CDF barrier with a neural network and relying on online sensor observations introduces uncertainties that must be considered during control synthesis. To address this, we develop a distributionally robust CDF barrier formulation for control that accounts for modeling errors and sensor noise without assuming a known underlying distribution. Simulations and hardware experiments on a UFactory xArm6 manipulator show that our neural CDF barrier formulation enables efficient planning and robust safe control in cluttered and dynamic environments, relying only on onboard point-cloud observations.