Humanoid Robot Whole-Body Motion Planning 2026
Humanoid Robot Whole-Body Motion Planning 2026
Whole-body motion planning for humanoid robots has entered a commercialization phase, with Figure AI filing at least 4 US patent applications in 2024–2026 alone. Hierarchical neural architectures and real-time QP solvers now compete as the dominant control paradigms.
Coordinating 30+ Degrees of Freedom in Real Time
Whole-body motion planning (WBMP) addresses the fundamental challenge of coordinating systems with 30 or more degrees of freedom to simultaneously satisfy balance constraints, task objectives, collision avoidance requirements, and actuator limits — all within real-time computation budgets. The field spans four interlocking sub-domains: trajectory optimization, whole-body control (WBC) frameworks, learning-based motion synthesis, and multi-modal locomotion.
The Versatile Locomotion Planning and Control framework (2021) illustrates how these sub-domains are increasingly merged, coupling TOWR+ trajectory optimization with an implicit hierarchical WBC using a composite rigid body model and centroidal inertia network. The MIT Humanoid Robot system (2021) further demonstrates actuator-aware kino-dynamic planners connected to MPC and whole-body impulse control for acrobatic behaviors including flips and spinning jumps.
Among the 60+ records retrieved, publication and filing dates span from 2005 to 2026, revealing three maturity phases: a Foundational Phase (2005–2013) establishing core mathematical formalisms, a Development Phase (2014–2020) maturing toward real-time performance, and a Commercialization Phase (2021–2026) driven by companies such as Figure AI building neural whole-body control architectures.
In this dataset, the patent record is highly concentrated among commercial entities. Figure AI dominates the most recent filings by volume in retrieved records, with Honda and Disney holding earlier foundational and active positions respectively. Among the 10 patents with jurisdiction data in this dataset, 8 are US-jurisdiction filings and 2 are WO (PCT) filings.
Filing Trends and Technology Cluster Distribution
Analysis of the retrieved patent and literature records reveals concentrated activity across four technology clusters, with optimization-based WBC representing the most densely populated cluster and learning-based approaches showing the fastest recent growth in filings and publications.
Technology Cluster Distribution — Patent & Literature Records (Dataset Snapshot)
In this dataset, optimization-based whole-body control is the most represented cluster, followed by learning-based synthesis, multi-modal locomotion, and human-demonstration methods.
↗ Click bars to explorePatent Filing Activity by Maturity Phase (Dataset Snapshot)
In this dataset, the Commercialization Phase (2021–2026) shows the highest patent filing concentration, with Figure AI’s 2024–2026 applications accounting for 4 of the most recent entries in retrieved records.
↗ Click bars to exploreKey Deployment Domains for Humanoid WBMP
Whole-body motion planning for humanoid robots is being applied across industrial automation, space exploration, healthcare, and brain-machine interface teleoperation, each imposing distinct real-time and safety constraints on the planning stack.
Industrial and Service Robotics
The largest application cluster in the dataset, covering manipulation in constrained factory and logistics environments. Figure AI’s Dynamic Task Assignment patent (US, 2026) addresses multi-robot coordination for whole-body task execution. The JET Humanoid Robot (2021) explicitly targets stair climbing and car egress for industrial and service settings.
Industrial AutomationSpace and Extreme Environments
Taikobot (2022) proposes a push-flight-park (PFP) whole-body planning strategy under microgravity using centroidal dynamics for intravehicular astronaut assistance. Harbin Institute of Technology’s Three-Layer Intelligence System (US, 2022, active) integrates digital twin with real-time whole-body control for hazardous exploration environments.
Extreme Environment RoboticsHealthcare and Eldercare Robotics
Multiple records motivate WBMP through eldercare and hospital deployments. The Rh-1 Full-Size Humanoid Robot (2010) explicitly targets hospitals, homes, and offices. HRL Laboratories’ System for Co-Adaptation of Robot Control to Human Biomechanics (US, 2021, active) targets physical human-robot collaboration requiring biomechanically adaptive whole-body planning.
Healthcare RoboticsBrain-Machine Interface Teleoperation
Brain-Machine Interfacing Control of Whole-Body Humanoid Motion (2014) demonstrates EEG-driven whole-body planning for a 36-DOF humanoid, with an autonomous planner filling the complement subspace beyond what the BMI controls. MIT’s Real-Time Anticipation and Synchronization patent (WO, 2026) extends this paradigm to close-proximity human-machine synchronization using task alignment distributions.
Teleoperation & HRILeading Assignees in Humanoid WBMP — Dataset Snapshot
In this dataset, Figure AI Inc. is the most active filer in the 2024–2026 window with at least 4 distinct US patent applications in retrieved records. Disney Enterprises and Honda Motor hold earlier active and foundational positions respectively, while HRL Laboratories and the University of Texas System represent notable active and PCT filings.
Top Assignees by Patent Filing Count in Retrieved Records (Dataset Snapshot)
↗ Click bars to exploreFigure AI Inc.
Figure AI is the most active filer in this dataset, with at least 4 US patent applications filed in the 2024–2026 window, all listed as pending. Their filings cover the Bipedal Action Model (BAM) hierarchical architecture for continuous real-time control over 30+ DOF humanoids, dynamic task assignment among communicating humanoid robot fleets, and whole-body control for complex dynamic settings. The BAM family includes a base application and two continuations, all filed as US pending in 2026.
United StatesDisney Enterprises Inc.
Disney Enterprises holds two active US patents on human demonstration-based robot action planning, filed in 2012 and 2015. Both patents use center-of-mass (CoM) trajectory from motion capture as a hard constraint in robot inverse kinematics, combining capture data with physics-based whole-body planning. Both patents are listed as active in this dataset, representing a maintained position in imitation-based humanoid motion planning.
United StatesFive Emerging Directions in Humanoid WBMP (2022–2026)
The most recent filings and publications in this dataset (2022–2026) point to five structural shifts: hierarchical neural whole-body architectures, human-synchronized planning, multi-robot coordination, full-body manipulation with hand-object coordination, and symbolic AI-driven closed-loop control.
Hierarchical Neural Whole-Body Architectures (BAM)
Figure AI’s 2026 BAM patent family — comprising a base application and two continuations, all US pending — represents a structural shift toward replacing or augmenting model-based WBC with end-to-end learned hierarchical action models trained on large motion datasets. The BAM architecture claims continuous, real-time control outputs over 30+ DOF systems in unstructured environments, capable of fluid human-like motion. This approach moves away from analytically derived QP solvers toward data-driven whole-body control.
Human-Synchronized Whole-Body Planning
MIT’s WO 2026 patent on Real-Time Anticipation and Synchronization introduces task alignment distributions projected onto the robot’s motion path to achieve synchronous rhythm with human collaborators. This moves beyond reactive collision avoidance toward genuine co-motion planning in close-proximity settings. Only one WO filing in this dataset directly addresses whole-body synchronization with human collaborators, making this an underpatented but rapidly emerging area.
Optimization-Based WBC vs. Learning-Based Motion Synthesis
Click any row to explore further.
| Dimension | Optimization-Based WBC (Hierarchical QP / MPC) | Learning-Based Motion Synthesis (DRL / Neural) |
|---|---|---|
| Core Mechanism | Hierarchical or weighted QP solvers translating task objectives into joint torques/velocities while enforcing contact, balance, and limit constraints | Deep reinforcement learning, imitation learning from motion capture, or end-to-end neural network controllers replacing or augmenting analytical planners |
| Representative Work | TOWR+ + IHWBC (2021); TALOS TSID benchmarking (2022); Fast five-mass analytical model at microsecond-scale (2020) | Figure AI BAM (US, 2026); ADHERENT motion-capture-trained WBC (Italian Institute of Technology, 2022); DRL + Twin Synchro-Control (Beijing Institute of Technology, 2020) |
| DOF Coverage | 30+ DOF via composite rigid body model and centroidal inertia network; handles full kinodynamic constraints | 30+ DOF claimed by Figure AI BAM; ADHERENT validated on iCub humanoid platform |
| Computation Speed | Fast five-mass model achieves microsecond-scale computation; QP solvers designed for real-time budgets | Real-time continuous output claimed by Figure AI BAM; inference speed depends on neural network size and hardware |
| Terrain / Environment | Non-flat terrain without integer programs (TOWR+/IHWBC); stair climbing and uneven terrain benchmarked on TALOS | Complex dynamic settings claimed by BAM; validated in structured sim-to-real transfer environments |
| Patent Status (Dataset) | Honda foundational patents (2009–2014 priority) now inactive; University of Texas WO 2016 middleware patent active | Figure AI BAM patents all US pending (2026); most learning-based methods appear as literature, not patents, in this dataset |
| Strategic Trend | Dominant in academic literature; facing displacement pressure from learning-based methods per dataset analysis | Fastest-growing cluster in recent filings; Figure AI building patent moat around neural whole-body architectures per dataset |
Frequently Asked Questions: Humanoid Robot Whole-Body Motion Planning
According to the retrieved records, the four interlocking sub-domains are: (1) trajectory optimization and kinodynamic planning using full-body dynamics or centroidal momentum models; (2) whole-body control (WBC) frameworks using hierarchical or weighted QP solvers; (3) learning-based motion synthesis using deep reinforcement learning, imitation learning, or neural network controllers; and (4) multi-modal locomotion and loco-manipulation across bipedal walking, crawling, climbing, and simultaneous manipulation tasks.
In this dataset, Figure AI Inc. is the most active filer with at least 4 US patent applications filed in the 2024–2026 window, all listed as pending. Their filings cover the Bipedal Action Model (BAM) hierarchical architecture for continuous real-time control over 30+ DOF humanoids, dynamic task assignment among communicating humanoid robot fleets, and whole-body control for complex dynamic settings.
Honda’s foundational whole-body control patents, with earliest priority dating to 2009, are now listed as inactive in this dataset. This suggests expiry or maintenance decisions and creates white space for competitors to build on task-space whole-body coordination concepts that were previously encumbered — representing a significant opportunity for new entrants in the 2025–2028 window.
The Bipedal Action Model is a hierarchical architecture filed by Figure AI (US, 2026, pending) that is trained end-to-end to produce continuous, real-time control outputs over 30+ DOF systems. It claims fluid human-like motion in complex dynamic settings. Strategically, it represents a structural shift toward replacing or augmenting model-based WBC with end-to-end learned hierarchical action models, and Figure AI has filed a base application plus two continuations forming a patent family around this approach.
According to the MIT WO 2026 patent in this dataset, human-synchronized whole-body planning introduces task alignment distributions projected onto the robot’s motion path to achieve synchronous rhythm with human collaborators. This moves beyond reactive collision avoidance toward genuine co-motion planning in close-proximity settings, where the robot anticipates and aligns its whole-body motion timing with that of a human partner.
In this dataset, Chinese academic institutions such as Beijing Institute of Technology and Harbin Institute of Technology appear prominently in literature records but are underrepresented in international patent filings. Among the 10 patents with jurisdiction data in this dataset, no CN, KR, or JP patents appear — only 8 US and 2 WO filings. The dataset notes this likely reflects retrieval bias rather than true geographic concentration, with Chinese domestic CN filings underrepresented.
Data and insights on this page are based on a limited patent and literature dataset and are for reference only. Figures may not represent the complete technology landscape.