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Physical AI and robot learning

Observation, action and task completion are considered together within the selected embodiment and environment.

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Specialist engineering programme
RGB / evidenceState / contextAction / intentShared clockRecovery labelsAccepted episode

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Capability dossier

The programme family

Observation, action and task completion are considered together within the selected embodiment and environment. Softinator connects specialist contribution to the engineering, information and operating requirements around the task.

Choose a focused engagement

The catalogue includes vision language action adaptation, robot imitation learning, robot reinforcement learning, humanoid teleoperation programmes, xr robot teleoperation. Each programme has its own contribution, artefacts and acceptance scope. Use the programme links below to identify the relevant engagement.

Commission the work

Bring the operating task, selected environment and result that matters. The engagement establishes scope, access, delivery responsibilities and the evidence used to assess the outcome. Connected platform and project pages provide the relevant context.

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Cross embodiment policy adaptationTransfer task representations across robot platforms with separate embodiment validation.Dexterous manipulation learningDevelop grasp and hand behaviours for selected objects and manipulation tasks.Embodied model evaluationAssess perception, action selection and recovery across representative robot tasks.Humanoid teleoperation programmesCapture controlled demonstrations and recovery scenarios for humanoid learning.Language conditioned manipulationConnect natural-language instructions to grounded physical actions.Mobile manipulation learningConnect navigation and manipulation in end-to-end physical task programmes.Robot dataset curationOrganise demonstration data into usable training and held-out evaluation datasets.Robot demonstration captureCollect coordinated observations, poses, instructions and outcomes for robot training.Robot imitation learningTurn demonstrations into learnable behaviours with task-level acceptance tests.Robot policy benchmarkingEvaluate task success and robustness within a defined physical testing scope.Robot recovery learningImprove response to failed grasps, disturbances and interrupted task execution.Robot reinforcement learningImprove robot task policies against specified physical objectives and constraints.Vision language action adaptationAdapt task-conditioned robot policies to selected embodiments and operating scenarios.XR robot teleoperationBuild immersive demonstration capture workflows with synchronised observations and actions.

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and the engineering underneath.

Our work

Cross-embodiment manipulation data

Coordinate frames and gripper semantics become a shared action representation that can be evaluated across embodiments.

Our work

XR humanoid teleoperation programmes

Spatial-control and demonstration programmes that connect operator quality, synchronised capture and humanoid trajectory evidence.

Trust centre

Enterprise procurement

Commercial scope, technical requirements and the evidence needed to commission the work.

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