Self-selected walking speed is a key outcome for exercise-based rehabilitation programs following lower-extremity trauma. This work introduces a novel reinforcement learning-based assist-as-needed (RL-AAN) controller for ankle exoskeletons, aimed at gait speed training. Built on an actor–critic architecture, the RL-AAN controller integrates a control objective that balances the trade-off between expected stride velocity (SV) errors and exoskeleton assistance. This approach allows the exoskeleton to progressively reduce ankle plantar- and dorsiflexion (PDF) assistance as the user’s performance improves, promoting active participation. The desired assistive torque is computed as the product of the actor output and the wearer’s biomechanical ankle PDF moment, estimated by a subject-agnostic model, thereby ensuring personalized and biomechanically relevant assistance. In a proof-of-concept study with healthy individuals walking on a self-paced treadmill with ankle weights, the RL-AAN controller outperformed a conventional Fixed-K controller—achieving greater immediate speed increases during assisted walking (14.2% vs. 10.0% relative to unassisted perturbed walking) and inducing short-term gait speed adaptation post-training, not observed with the conventional controller. These findings highlight the potential of RL-AAN control for subject-tailored gait training, with promising clinical implications for exercise-based rehabilitation in individuals with neurological or musculoskeletal gait impairments.

This paper presents methodologies to accelerate the registration of 3D point cloud segments by using hue data from the associated imagery. The proposed variant of the Iterative Closest Point (ICP) algorithm combines both normalized point range data and weighted hue value calculated from RGB data of an image registered 3D point cloud. A k-d tree based nearest neighbor search is used to associated common points in {x, y, z, hue} 4D space. The unknown rigid translation and rotation matrix required for registration is iteratively solved using Singular Value Decomposition (SVD) method. A mobile robot mounted scanner was used to generate color point cloud segments over a large area. The 4D ICP registration has been compared with typical 3D ICP and numerical results on the generated map segments shows that the 4D method resolves ambiguity in registration and converges faster than the 3D ICP.