IEEE Robotics and Automation Letters

Planetary exploration missions increasingly rely on agile robotic platforms capable of traversing unstructured and unpredictable terrain. While quadruped robots offer superior mobility compared to wheeled rovers, their deployment in long-duration and shock-load missions remains limited, due to the limitations of high power consumption, low payload-to-weight ratio, and poor resilience to impact an…

We introduce a platform of oscillating eel-like robots that exploits physical collisions to enable controllable collective behaviors. Each robot is actuated by a Kuramoto-style central pattern generator, and produces undulatory motion that passively synchronizes through collisions with neighboring robots and a surrounding rigid membrane. Instead of avoiding collisions, the system leverages robot-…

Motion planning is a critical aspect of lower-limb exoskeleton control. Conventional motion planning approaches compute reference trajectories in task space, leading to infeasible or uncomfortable joint level motions. Conversely, joint space planning methods ensure feasibility but may fail to reproduce natural human gait, limiting their effectiveness in rehabilitation. This paper introduces a nov…

Untethered milli-scale robots hold promise for navigating confined biological pathways and delivering localized interventions, yet existing designs often rely on solid geometries or modular assemblies that limit robustness, payload capability, and locomotion versatility. A single-unit milli-scale voxel robot is introduced, featuring a cuboctahedral open-frame architecture with strategically embed…

In human–robot interaction (HRI), detecting a human's gaze helps robots interpret user attention and intent. However, most gaze detection approaches rely on specialized eye-tracking hardware, limiting deployment in everyday set- tings. Appearance-based gaze estimation methods remove this dependency by using standard RGB cameras, but their practi- cality in HRI remains underexplored. We present a …

Monocular depth estimation foundation models provide robust depth priors with exceptional generalization capabilities; however, their predictions typically lack a reliable metric scale and contain local inconsistencies in a zero-shot setting, which limit their deployment in unconstrained real-world environments. Meanwhile, sensor-based depth measurements are typically sparse or noisy, especially …

Clay sculpting is a nuanced, artistic task involving dexterous manipulation with long-horizon planning to achieve high-level goals. As a robotics problem, we formulate clay sculpting as a shape-to-shape matching challenge. Prior deformable object manipulation work either requires retraining a policy per goal or relies on dynamics models which represent state as sparse point clouds which do not ca…

Designing reward functions for reinforcement learning (RL)-based quadruped locomotion often requires extensive trial-and-error, limiting efficiency and interpretability. Lack of interpretability is particularly critical for large-scale hydraulic quadrupeds, where undetected unstable behaviors during deployment can cause significant mechanical damage. This paper presents a training framework that …

Accurate three-dimensional (3D) shape sensing is vital for continuum robots in minimally invasive surgery. Conventional optical fiber methods depend on multi-fiber or multicore configurations, increasing integration complexity and associated costs. Single-fiber approaches support miniaturization, but struggle to decouple 3D bending and twist. We propose a single-channel single-fiber framework bas…

This paper addresses vision-based autonomous landing of quadrotor drones on moving platforms with uncertain motion. Vision is attractive due to its low weight, low cost, and ability to provide direct relative observations without global reference frames. However, traditional visual landing relies on requiring accurate estimation and tuning, which limits robustness. Deep reinforcement learning (DR…

End-to-end autonomous driving has emerged as a promising paradigm. However, state-of-the-art methods rely heavily on Transformer architectures. The inherent quadratic complexity of Transformers restricts their ability to model long-range spatial and temporal dependencies, particularly on resource-constrained edge platforms. Given the inherent demand for efficient temporal modeling in autonomous d…

Soft autonomous underwater vehicles (AUVs) that employ biological swimming mechanisms have demonstrated immense promise in navigating marine environments with increased efficiency and adaptability and decreased environmental disturbances in comparison to their rigid counterparts. However, most AUVs with soft propulsors are poorly suited for precise swimming at low speeds and station holding tasks…

Soft robots, inspired by elephant trunks or octopus arms, offer extraordinary flexibility to bend, twist, and elongate in ways that rigid robots cannot. However, their motion planning remains a challenge, especially in cluttered environments with obstacles, due to their highly nonlinear and infinite-dimensional kinematics. Here, we present a graph-based path planning tool for an elephant-trunk-in…

The reprojection error in Visual-Inertial Odometry (VIO) suffers from high nonlinearity due to perspective division, which degrades estimator consistency and robustness, particularly under large depth uncertainty. To address this, we propose a novel visual measurement model, the Orthogonal Ray Projection Error (ORPE), which is formulated in the tangent space of the observation ray. By minimizing …

Safety-critical control using high-dimensional sensory feedback from optical data (e.g., images, point clouds) poses significant challenges in domains like autonomous driving and robotic surgery. Control can rely on low-dimensional states estimated from high-dimensional data. However, the estimation errors often follow complex, unknown distributions that standard probabilistic models fail to capt…

In human-robot interaction, external force measurement is fundamental to achieving robot compliance control. Parameter identification based on robot dynamics enables external force detection without expensive sensors. However, the unmodeled dynamic errors inherent in real robots pose a challenge to force estimation accuracy. In addition, existing force estimation methods often suffer from high co…

This paper presents a two-dimensional planar path-following controller for active-joint active-wheel snake-like robots. Compared to passive-wheel snake robots, active-wheel snake robots can navigate narrower spaces and generate stronger driving forces, enabling adaptation to various terrains. However, due to the highly redundant multi-link and multi-active-wheel body configuration, achieving whol…

The robotic peg-in-hole assembly task remains challenging. Traditional force control methods struggle with complex parameter identification and contact state analysis, while deep reinforcement learning(DRL) suffers from low efficiency and poor adaptability. To address these shortcomings and to capitalize on the strengths of both, this paper presents a fuzzy fusion control strategy and improved De…