Apple's research team published the paper "Perceptive Humanoid Motion from Simulation" on arXiv, unveiling HALO, an autonomous control system for humanoid robots, and simultaneously open-sourcing the training and deployment code.
The authors propose the first mmWave radar-based onboard perception-and-control dynamic obstacle avoidance system for small quadrotor UAVs. The system combines a lightweight interacting multiple models (IMM) tracker with a control barrier function (CBF) controller that directly outputs evasive accelerations, and derives sufficient avoidance conditions relating sensing range, relative speed, and control delay.
The paper uses an evolutionary algorithm to search for multicopter body morphologies suited to different objectives and constraints, and uses the non-dominated sorting genetic algorithm II (NSGA-II) to generate a set of bodies with performance trade-offs. Compared with a standard quadcopter, the Pareto front extrema improve maneuverability, thrust-to-weight ratio, and size by 489.2%, 23%, and 4.8%, respectively; the maneuverability improvement comes from motor tilt angles, and the thrust-to-weight ratio improvement comes from more propellers. Real drone racing experiments show that the evolved bodies outperform the standard body in number of gates passed, flight speed, and lap time.
LACART combines lookahead-based control barrier functions (CBFs), a relaxed control Lyapunov function (CLF), and quadratic programming (QP) to modify nominal vehicle commands in a unified optimization layer, handling both circular obstacles and track boundaries.
DKWBC proposes a dynamics-informed whole-body control framework that uses inverse-dynamics feedforward and joint PD feedback to supply tracking torques, while a residual policy learns the joint-torque corrections needed for balance. The authors state that this structure explicitly separates motion-related torque demands, simplifying the policy learning task, and that they validated in simulation and on real hardware that a single balancing policy can generalize across multiple motion datasets, maintaining tracking and balance under ground support, added payload, and physical interaction. The project homepage has been made public.
SUGARv2 proposes a unified framework for learning diverse visual humanoid loco-manipulation skills through interaction reference synthesis and a shared training pipeline. The method adapts reusable task templates to different spatial configurations to synthesize coordinated robot and object motion references at low cost, and uses reference-based imitation as a common learning objective across tasks, first training privileged teachers and then training visual policies through teacher distillation and reinforcement learning.
The authors propose TPS-A*, a lattice planner whose edges are kinematically feasible trajectories produced by parameterized trajectory generators, with certified collision checking performed in trajectory parameter space. A grid precomputed per footprint needs only a single pass over local obstacles to return a certified free distance for every candidate trajectory, making the per-node collision cost independent of footprint complexity and trajectory count.
The paper proposes a safety-critical control framework for nonholonomic multi-robot systems that lets each robot circumnavigate a stationary target at a different orbital radius while maintaining spacing from neighbors and avoiding collisions. Each robot measures only its distance to the target and exchanges information only with its two adjacent robots; it drives the radius to the target value via acceleration, balances the formation via turn-rate, and uses a higher-order control barrier function quadratic program (HOCBF-QP) to simultaneously constrain target distance, geofence, and inter-robot safety.
FastFunnels proposes a hierarchical multi-robot navigation method: a high-level reinforcement learning policy generates deformable elliptical envelopes in real time, while a low-level distributed model predictive control (DMPC) layer keeps robots within the envelopes and avoids collisions with each other.
FATHOM proposes a world-model-guided diffusion action-trajectory selection framework for relative tracking of autonomous underwater vehicles (AUVs) under sparse active light markers. The method first fuses marker observations, inertial measurements, action history, and temporal dynamics using a multimodal task-supervised recurrent state-space model to form a predictive tracking belief; a diffusion action prior then generates multiple multi-thruster trajectories, and the best candidate is selected under a receding-horizon objective and continuously updated based on onboard observations.
ForPC is a force-aware whole-body control framework that regulates a humanoid robot's cart-pushing speed through two-stage reinforcement learning. The method first infers force latent variables and state latent variables from onboard observations without external force sensors, then uses a residual policy to coordinate arm, waist, and leg movements; in simulation, cart-pushing speed RMSE is reduced by up to 68.2% compared with a locomotion-only control baseline, and lateral error on multi-curve routes is reduced by up to 74.2%.
Apple's research team published the paper "Perceptive Humanoid Motion from Simulation" on arXiv, unveiling HALO, an autonomous humanoid robot control system, and validating it on the Unitree G1.
Why it matters: Apple's HALO splits task orchestration, motion generation, and tracking into three layers, and reports comparative results of 91% in unfamiliar environments and 70% on composite tasks, offering a look at its layered design and data synthesis pipeline.
SuperMimic proposes a humanoid robot motion-tracking framework that uses a differentiable world model with 32-step backpropagation rollouts to reduce training compute demands when the motion library is large. The framework models physical laws with a deterministic incremental model and uses real states as the starting point for imagined rollouts. The original is an in-depth analysis; the body provides only a summary.
Apple released the HALO humanoid whole-body motion control system, which procedurally generates 1,000 virtual residential scenes and synthesizes nearly 300 hours of paired data, enabling the Unitree G1 to master skills such as sitting on chairs, turning sideways to pass through a half-open door, crouching under a table, and pushing boxes.
Why it matters: Apple's HALO uses simulation-synthesized data to train humanoid whole-body skills, transfers them zero-shot to real scenes, and ablation experiments show the contribution magnitude of each module.
Centered on Cosserat rod mechanics, curvature traveling-wave actuation, and anisotropic friction, PyElastica uses three sets of simulations to demonstrate the jointless serpentine gliding mechanism of a continuum bionic snake. The article also examines the actual effect of superimposing a lifting wave on a fixed gait.
A review proposes a functional "decoder-shaper-controller" framework for continuous myoelectric control of upper-limb exoskeletons: the decoder estimates intention-related variables from surface electromyography (sEMG), the shaper maps them to a control reference according to an assistance policy, and the controller realizes that reference through actuator control.
The authors propose an uncertainty-aware active exploration framework that can autonomously learn task-agnostic, generalizable dynamics models for soft robots. The method was evaluated on three simulation platforms—a continuum arm, an articulated fish in fluid, and a musculoskeletal leg—as well as on a real pneumatically actuated continuum arm. Compared with random exploration and task-specific baselines, it achieves higher modeling accuracy and yields higher returns in zero-shot control on unseen tasks, while remaining robust under sensing noise, actuation delays, and material nonlinearities.
The research team proposes HALO, a humanoid robot autonomy system that synthesizes vision-language-action data across roughly one thousand procedurally generated home scenes, eliminating reliance on teleoperation data collection. HALO learns skills including sitting on chairs, navigating to specified objects, stepping sideways through half-open doors, crouching and crawling under tables, and pushing boxes, and can combine them to complete long-horizon tasks. Without ever seeing real home scenes during training, HALO transfers zero-shot to the physical world, achieving a 91% success rate across 360 trials in six unmapped real-world environments.
Why it matters: The paper presents a complete path from synthesizing vision-language-action data using procedurally generated home scenes to zero-shot transfer to real robots; its approach to data scaling is worth referencing.
The research team learned an adiabatic spectral submanifold (aSSM) model of the full body of a trimmed-helicoid soft trunk robot from data and used it in model predictive control (MPC), completing a range of shape control tasks on hardware. In closed-loop dynamic shape regulation, the method outperformed existing linear baselines by a factor of 10; in human-in-the-loop interactive demos, it completed section-by-section shaping and balanced a tennis ball for more than 25 seconds, whereas the linear baselines failed to complete the task. A model-free reinforcement learning policy trained on hardware for 12 hours failed on the same balancing task.
The authors propose soccerbot, a hierarchical reinforcement learning framework that distills a structured latent motion prior from large-scale human soccer motion capture data, constraining policy learning within a dynamically feasible motion manifold. A single skill-conditioned policy thereby learns moving, dribbling, trapping, and kicking in a unified way, while maintaining human-like motion and fluid transitions during skill switching. After robustness-oriented simulation design, the framework transfers zero-shot to the Unitree G1 humanoid robot, completing the full soccer skill set in real-world environments.
The authors propose H^2MARL, a hierarchical heterogeneous multi-agent reinforcement learning framework for humanoid whole-body control. The method performs policy factorization with physical heterogeneity awareness at the joint degree of freedom (JDoF) level, conditioning each torque-actuated joint on descriptors of its own actuation, kinematic, and dynamic properties, and uses closed-loop policy-executor co-optimization to jointly adapt the high-level pose planner and low-level joint executor with shared interaction trajectories.
The study proposes a unified planning and control framework for mobile continuum robots, combining a global layer consisting of A* search and minimum-jerk planning with a local layer consisting of a geometry-consistent inverse kinematics solver and Riemannian optimization, to coordinate the mobile base and continuum deformation for obstacle avoidance. The authors compare it with multiple baseline planners in simulated pipe-traversal tasks and conduct real-world experiments on navigation among dense obstacles and manipulation in confined spaces. Results show improvements in motion consistency, feasibility, and adaptability.
This study examines the visual stabilization performance of boundary-layer sliding mode control (SMC) relative to causal baselines such as LPF and PID under plant-binding conditions, where disturbance velocity approaches or exceeds the actuator slew limit and the per-step actuation budget is constrained.
The paper proposes Future-Window Inverse Kinematics (FWIK), an online inverse kinematics selection method that considers several future waypoints, connects multiple IK candidates in a layered joint-space graph, ranks them with a motion-time estimate, and then uses a learned model to remove transition paths prone to self-collision.
This paper proposes a contact-implicit whole-body optimization framework for humanoid robots to generate dynamic motion trajectories on parameterizable complex terrains such as steps and ramps, without requiring a predefined contact sequence. The method approximates ground geometry with smooth surfaces and extends rigid-body interaction from planar surfaces to arbitrary smooth surfaces through new contact constraints, using nonlinear programming to plan motions across various terrain topologies. The authors conducted preliminary feasibility tests on non-coplanar terrains in simulation and on the real robot ErgoCub.
The team led by Wu Xinyu at the Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, has developed a wearable underwater exoskeleton. Its representative system, PEAKED, is a soft underwater hip exoskeleton that assists hip flexion and extension, supporting flutter kicking, breaststroke kicking, and underwater walking. In prior human experiments, PEAKED reduced per-minute breathing volume by 29.77% during flutter kicking, 25.70% during breaststroke kicking, and 18.35% during underwater walking.
A team at Tohoku University has developed a mechanical reflex module that requires no electronic sensors or circuits. Integrated into a musculoskeletal robot, it reproduced the knee-jerk reflex, and combined with high-level control that adjusts activation thresholds according to movement phase, it achieved bipedal walking. The module integrates sensing, computation, and actuation within its physical structure: it mechanically detects when an artificial muscle is stretched beyond a threshold and contracts automatically, inspired by human muscle spindles.
Flapping Flyer reproduces a 77 mg flapping-wing aerial vehicle in MuJoCo, modeling it with a three-term blade-element aerodynamic force composition, and achieves hovering with LQG closed-loop control, with altitude fluctuation of 0.12 mm. The material also mentions wing wash, but the main text does not elaborate on it.
A study published in the International Journal of Automation and Control proposes a genetic algorithm-optimized fuzzy controller (GA-FLC) that reduced average root mean square error by 89% compared with conventional feedback control in simulations of a three-degree-of-freedom robotic arm.
An ETH Zurich team has developed FlexiQuad, a ring-shaped flexible-frame quadrotor with no central body; on impact the frame deforms to absorb energy and the drone can keep flying. It reaches a top flight speed of 80 km/h, with collision tests at about 25 km/h; the frame material costs less than 15 Swiss francs, and it has been used to grasp and carry soft first-aid kits and payloads heavier than its own weight. The team also used reinforcement learning to let it stabilize its attitude after a collision before resuming autonomous flight, but reliable perception and autonomous navigation remain prerequisites for leaving the lab.
Zou Danping's team at Shanghai Jiao Tong University proposes DiffDreamer, which couples a learned world model with differentiable simulation for agile quadrotor control; the paper has been accepted to RAL 2026. The method uses the world model to predict external dynamics and differentiable simulation to propagate the robot's physical state. In aggressive tracking tasks, distance error drops to 0.381 m, versus 0.793 m for the strongest baseline; in dynamic gate crossing at gate speeds of 1.0/1.5/2.0 m/s, the number of gates passed is 16/12/8, versus 12/4/1 for MPC.
G1 Brunson uses event-based rewards and asymmetric actor-critic training, bringing the depth camera directly into the observation loop during training to form a G1 whole-body dribbling policy. Reproducing the official weights locally in MuJoCo gave an average of 43.5 crossovers over 12 episodes with zero falls, matching the official results.
WAM trains a forward-dynamics world model on quadruped robot locomotion data collected under convex model predictive control, replaces hand-crafted steering laws with receding-horizon sampling planning, and combines A* global paths with dynamic obstacle extrapolation to achieve navigation and obstacle avoidance. The material states that all numerical values come from local reproduction experiments.
A research team at Seoul National University has proposed RISE (Responsibility-Induced Specialized Experts), a VLA framework that can collect demonstration data for locomotion and object manipulation separately and then learn jointly from them, enabling a robot to execute multi-stage tasks consecutively without full multi-stage demonstrations.
A University of Bristol team built a tabletop physical model simulating muscle contraction using a row of ordinary electric motors coupled to each other through a shared mechanical skeleton, with results published in the Royal Society journal Interface. In the experiments, the motors made intermittent contact with the skeleton to transmit force, and without direct communication, muscle-like behaviors spontaneously emerged, including a load-velocity relationship, more units participating as load increased, and coordination waves. The researchers believe this approach, in which coordination is handled by the structure itself, could help improve how robots are actuated.
The SoftCon framework builds an octopus swimming control pipeline from tetrahedral soft-body dynamics, muscle constraints that contract along fibers, and traveling activation waves generated by keyframe matrices and propagation filtering, using PPO to learn coordination rather than reconstruction. The framework originates from SIGGRAPH Asia 2019.
G1 Dribble uses end-to-end reinforcement learning to achieve humanoid robot soccer dribbling; the walking task's weights are not adjusted, and velocity commands drive both the body and the ball. The author tested it locally in MuJoCo-Warp and recorded eighteen cloud sessions, reporting touch rhythm, the distance band for re-touching the ball, and failure modes, including that a centered start steps over the ball without touching it, a first touch occurs only when offset to the side, and at 1.5 meters per second it knocks the ball away directly.
Neural-Fly proposes a residual aerodynamic force adaptation law that combines wind-invariant representations with online least squares for online adaptive control of quadrotors in strong winds. The method trains a network on public flight data and conducts comparative figure-8 trajectory tracking experiments with a PyBullet quadrotor in a steady wind field, demonstrating rapid adaptation enabled by domain-shift separation.
The paper proposes a Transformer-based architecture with shared modular recurrence for universal morphology robot control and evaluates its generalization performance on a set of MuJoCo robots. The authors note that contextual features are often only partially available but can be recovered through modular interactions, improving multi-robot control and generalization to contexts unseen during training. Results show substantial improvement in zero-shot generalization performance on robots with unseen dynamics, kinematics, and topologies across four environments.
The paper proposes actuator-aware (AA) observer inputs: the altitude and attitude observers use limited virtual inputs filtered by an assumed rotor model and aligned with the measurement delay, while the horizontal observers use acceleration reconstructed from measured attitude.