Markov Robotics offers a hardware‑software platform that combines a 7‑DOF multi‑fingered gripper with real‑time perception to perform zero‑shot dexterous manipulation of unseen objects. The system translates raw point‑cloud or RGB‑D inputs into pick‑and‑place, in‑hand reorientation, and assembly primitives via a high‑level Python/ROS API, cutting integration cycles from weeks to hours. It is sold as turnkey robot units and licensed algorithm modules for manufacturers and research laboratories.
Funding
Funding not disclosed
Founders
Product
Problem
Industrial and research robots typically require extensive task-specific programming or large datasets to manipulate new objects, which leads to long integration cycles and high engineering costs when product lines or experimental setups change. This rigidity hampers the ability to quickly adapt to novel parts, custom assemblies, or research prototypes.
Solution
Markov Robotics delivers a combined hardware‑software platform that enables zero‑shot dexterous manipulation. The system leverages a perception‑driven policy network that generalizes to unseen objects without prior demonstration, allowing robots to grasp, reorient, and assemble items out‑of‑the‑box. Integrated multi‑fingered end‑effectors provide high‑resolution force control, while the onboard compute runs real‑time inference to close the perception‑action loop. Users can program new tasks through a high‑level API rather than low‑level motion scripts, reducing integration time from weeks to hours. The platform is packaged as a turnkey robot unit and as licensable algorithm modules for OEM integration.
Target Audience
Primary customers are manufacturers seeking flexible automation for variable part handling and research laboratories that require rapid prototyping of manipulation tasks.
Features
- Zero‑shot learning pipeline that maps raw point‑cloud or RGB‑D input to manipulation primitives without offline training on the target object
- 7‑DOF multi‑fingered gripper with tactile sensing and impedance control for sub‑millimeter positioning and force regulation
- Real‑time perception stack (GPU‑accelerated vision transformer) delivering 30 fps object pose estimation and contact prediction
- Modular hardware architecture with interchangeable end‑effectors and plug‑and‑play sensor suites (force/torque, vision, proximity)
- Python/ROS‑compatible SDK exposing high‑level “pick‑and‑place”, “in‑hand reorientation”, and “assembly” primitives
- Sim‑to‑real transfer framework that validates policies in a physics‑accurate simulator before deployment, minimizing on‑site tuning
- OTA firmware and model updates with built‑in safety checks and ISO‑26262 compliance for industrial use