Live manipulation console • simulated data

The hard part of robotics isn't thinking. It's doing.

PRAXIS is Ace Hacker's physical-intelligence engine. It perceives cluttered workspaces in 3D, manipulates objects with dexterous, force-controlled grasps, learns new skills from demonstration and simulation, and orchestrates fleets of robots with quantum-scheduled task and motion planning.

Request a demo → Explore the engine

sub-mmgrasp repeatability

unknownobject generalization

hourssim-to-real, not months

praxis://cell-02/manipulate-live GRASP OK

6-DoF arm • live

grasp confidence0.97

Pick success

99.2%

Cycle time

0.38s

Payload

4.2kg

Engine at a glance

Pick success on unknown objects

Manipulate

Median pick-place cycle time

Perceive

Skills in the shared library

Learn

Robots orchestrated per site

Orchestrate

One engine, four instruments

From pixels to physical action.

PRAXIS pairs each layer of manipulation with the right computation, deep learning to see and grasp the physical world, sim-to-real to acquire skills fast, and quantum optimization to schedule and coordinate robots at facility scale.

Perceive

3D perception

Reconstructs the workspace, estimates 6-DoF object pose, and finds where an object can be grasped.

pose • affordance • scene

Manipulate

Dexterous grasping

Synthesizes and executes grasps under contact and force, from pinch to power, on objects it has never seen.

grasp synthesis • force control

Learn

Sim-to-real skills

Acquires new skills from a handful of demonstrations plus massively-parallel simulation, and transfers them to hardware.

imitation • RL • sim2real

Orchestrate

Fleet scheduling

Coordinates many robots and schedules their tasks and motions across a facility with quantum solvers.

QAOA • job-shop • motion

Perceive • Instrument 01

You can't grasp what you can't locate.

A bin of jumbled parts, reflective, overlapping, half-occluded, is the everyday reality of manipulation. Perceive reconstructs that clutter in 3D, estimates each object's full 6-DoF pose, and predicts where on it a grasp will actually hold.

01
6-DoF pose estimationEach object's position and orientation are recovered from RGB-D, even under occlusion and clutter, with a calibrated confidence.
02
Grasp affordanceA learned model predicts, per surface patch, how likely a grasp there is to succeed, turning geometry into graspable regions.
03
Scene reconstructionDepth is fused across views into a clean occupancy model of free and blocked space for the planner.

RGB-D fusion6-DoF poseaffordancesegmentation

Grasp affordance mapgraspability

per-region grasp success prediction

UngraspableMarginalHigh affordance

6-DoF pose confidenceper object in bin

Depth • occupancynear = brighter

Manipulate • Instrument 02

Contact is where robotics gets hard.

The instant a gripper touches an object, geometry gives way to physics: friction, deformation, slip. Manipulate synthesizes a grasp, then closes the loop on force and contact so the robot holds firmly, inserts precisely, and never crushes what it picks.

Contact force during insertionpeg-in-hole

measured force vs target envelope

Contact forceSafe envelopeSeated

01
Grasp synthesisCandidate grasps are generated and ranked by a learned quality metric, from precision pinch to enveloping power grasp.
02
Force and impedance controlThe controller regulates contact force, not just position, so contact-rich tasks like insertion and assembly succeed.
03
Slip detection and regraspTactile and visual feedback catch a slipping object and trigger a regrasp before it is dropped.

grasp qualityimpedance controltactileregrasp

Candidate grasps • ranked qualitytop-K

Per-joint torque7-DoF arm

Learn • Instrument 03

Teach it once. It practices a million times.

A new task should not need a robotics PhD and six months. PRAXIS learns a skill from a few human demonstrations, then refines it across thousands of parallel simulations with randomized physics, and transfers the result to the real robot in hours.

01
Learning from demonstrationA few teleoperated or kinesthetic demos seed a policy, no reward engineering required to get started.
02
Massively-parallel simThe policy is refined with reinforcement learning across thousands of randomized simulated environments at once.
03
Sim-to-real transferDomain randomization closes the reality gap, so a skill learned in sim works on hardware with minimal on-robot tuning.

imitationRLdomain randomizationpolicy transfer

Sim-to-real success curvetransfer gap 3%

task success vs training steps

SimulationReal robotDemo baseline

Policy rewardRL fine-tuning

Domain randomizationmass × friction

Skill generalization • skill × object classsuccess rate

Orchestrate • Instrument 04

One robot is a tool. A coordinated fleet is a factory.

Deciding which robot does which task, in what order, without arms colliding or work stalling, is a scheduling problem that explodes with fleet size. Orchestrate casts facility-scale task and motion scheduling as optimization and solves it with quantum and hybrid methods.

Multi-robot schedulemakespan optimized

task assignment across robots & time

PickPlaceTransitCharge

01
Task allocationJobs are matched to robots under reach, capability, and deadline constraints, a large assignment problem.
02
Job-shop schedulingSequencing to minimize makespan is cast as a QUBO and solved with QAOA and hybrid methods.
03
Collision-free coordinationShared workspaces are deconflicted so arms and mobile robots never contend for the same volume.

QAOAQUBOmakespandeconfliction

Scheduler convergencemakespan (norm.)

Cell throughputpicks / hour

Benchmarks • honest numbers

Where quantum earns its place, and where it does not.

We benchmark against strong classical baselines, tuned grasp planners and heuristic schedulers. Learning wins on perception and manipulation; quantum optimization wins as fleet scheduling grows combinatorial. PRAXIS routes each job to the method that wins, and keeps the real-time control path classical.

Schedule solve time vs fleet sizelower is better

Classical schedulerPRAXIS hybrid

Capability profilevs conventional stack

Architecture • perception to fleet

A loop that closes on contact.

Perception feeds grasping; grasping feeds real-time control on the robot; every attempt, success or failure, feeds the learning loop that sharpens the next one; and the fleet scheduler keeps every robot busy and safe.

Now deploying with partners

Put physical intelligence on your line.

PRAXIS is in early deployments in warehousing, manufacturing, and humanoid platforms. Bring us your hardest bin, your trickiest assembly, your busiest cell, and we will run it through the engine.

Request a demo → Read the technical brief

Runs on your robots • arm-agnostic • your task data stays yours