robotics hardware designers

Abstract: A dexterous hand can change an object's pose without setting it down. This survey covers 113 learning-based methods that attempt it, the hands they run on, the simulators they train in, and how they report results. The field has largely converged on one recipe: reinforcement learning on privileged state in a GPU simulator, distilled into a vision-only student for transfer to hardware. Almost half the corpus trains that way, nearly all of it in one simulator, whose own successors are barely used. A second and faster-growing branch learns from teleoperated demonstration instead, through vision-language-action models, diffusion policies, and systems whose contribution is the collection rig itself. Published work has tripled since 2022. Grasping remains the largest task family and bimanual coordination the fastest-rising, and almost every experiment runs on one of four hands. What the survey adds comes from reading released code and vendor specifications beside the papers. Eight hands that can be bought or built from published designs appear in no method paper here. Where the depth a hand sinks into the object it holds is reported at all, it is not measured in the physics that produced the motion. Section VII proposes a protocol for the unreported quantities; the body gives the counts behind every claim above.

Abstract: When a mobile terminal takes part in over-the-air training, its own motion rotates the gradient that travels back through it; a movable antenna that keeps pace with the terminal rotates it back---without any channel estimate. We give a speed law: under a scalar conditional-mean model of the physical return, landing the residual displacement inside the first positive lobe of the spatial correlation keeps the conditional mean gradient a descent direction, which, given sufficient travel, fixes a critical actuator-to-terminal speed ratio. We further prove a bias--jitter law: under the same scalar conditional-mean model and with zero effective bias, independent zero-mean Gaussian jitter of any finite amplitude scales the conditional mean gradient without reversing its direction, whereas a sub-wavelength systematic bias can reverse it. This result motivates prioritizing bias calibration to preserve gradient direction. Simulations on 3GPP CDL-A channels show that an antenna keeping pace with the terminal achieves near-CSI accuracy without digital channel correction, reaching 98.58 % compared with 98.59% for the ideal-CSI reference. The simulations also show that partial compensation can support learning when the residual displacement falls within a later positive correlation lobe, so a faster actuator does not necessarily yield higher accuracy. Separate positioning simulations show a sharp accuracy loss under systematic bias but little degradation under zero-mean Gaussian jitter of comparable spatial scale.

Abstract: Space robots must interpret tasks and act despite unfamiliar dynamics, difficult sensing conditions, and limited opportunities for human intervention. Foundation models offer reusable perceptual and behavioral knowledge, but their value depends on how that knowledge reaches physical execution. This critical review follows space-specific requirements through perception, prediction, planning, and action to deployment and validation, emphasizing orbital manipulation and proximity operations. Terrestrial comparisons establish benefits of particular pretraining stages and model interfaces; their transfer to space dynamics and constrained onboard operation remains insufficiently tested. Event cameras offer timely motion observations across strong illumination contrasts, while spiking neural networks provide possible routes to sparse local computation. We examine how sensor degradation, state estimation and computing choices affect feedback and recovery. Higher action throughput need not yield fresher feedback; lower processor power can also coexist with higher energy per inference. We organize validation as an iterative sim-to-ground-to-space pathway, connecting physical tests with telemetry-based diagnosis and closed-loop revalidation. A satellite-capture example links observations to approach, contact and recovery, while a corresponding protocol separates generalization from adaptation with limited target-domain interaction. These comparisons identify what models already contribute and the physical evidence needed to support autonomous space operation.

Abstract: Biological visual navigation achieves remarkable robustness at extremely low power, whereas frame-based robotic systems relying on artificial neural networks remain limited in both respects, particularly for energy-constrained micro aerial vehicles (MAVs) operating in complex environments. Inspired by the event-driven, sparse, and asynchronous principles of biological visual systems, neuromorphic sensing and computation provide a promising alternative for robust and energy-efficient navigation. In this paper, we propose the first end-to-end event-driven neuromorphic paradigm for MAV visual navigation in complex environments by integrating neuromorphic vision, spiking neural networks, and neuromorphic processors. The proposed method first extracts the environmental representation from event streams using a spiking perceptual representation module. This representation is fed into a spiking navigation module, where guidance learning and population coding enable efficient policy learning with few time steps. The navigation module then outputs a waypoint converted into executable trajectory points. Experiments show that the proposed neuromorphic method achieves navigation performance comparable to ANN-based methods while markedly reducing power consumption. The spiking perceptual module consumes only 19.57% of the power of its ANN counterpart, while the spiking navigation module deployed on Intel Loihi consumes only 12.37% of that required by its ANN counterpart running on NVIDIA Orin NX. Beyond prior neuromorphic systems focused mainly on relatively simple navigation subtasks, we demonstrate a complete neuromorphic visual navigation system spanning simulation and real-world deployment. Our results establish the feasibility of the neuromorphic paradigm for autonomous navigation and provide a bio-inspired pathway toward robust and low-power MAV navigation.

Abstract: Passive leader arms transmit an operator's motion without returning remote contact or load cues. TacGELLO uses the existing servos of a 3D-printed leader for tactile contact feedback at the gripper trigger and current-based load cues at the arm joints. AnySkin contact detection activates a trigger position hold, while changes in follower joint current adjust leader-servo output limits to command resistance during lifting. Across six UR3 sessions with one operator, sent-command tracking error was 0.15--0.36$^\circ$ RMSE, with estimated trajectory lag of 82--98\,ms. Tactile onset was logged in 60 of 62 segmented TacGELLO grasp intervals. Median commanded closure after tactile or width-based contact references was 0.7--2.1\,mm with TacGELLO and 13.0--13.9\,mm with the original interface.