Hardware & control
Dexterous manipulation
Dexterous manipulation is the controlled, skillful reconfiguration of an object through coordinated motion and contact, often using multiple fingers. It can involve changing an object's pose within a hand, regrasping, sliding, rolling, finger gaiting, or making precise contact with the environment. The term describes capability, not a fixed minimum number of fingers or joints.
Also known as: robot dexterous manipulation, dexterous robotic manipulation
Updated
Dexterity is more than holding an object
A stable grasp keeps an object from moving unintentionally. Dexterous manipulation deliberately changes the object or its contact configuration. Bicchi's influential robot-hand survey describes dexterity in terms of moving a manipulated object from one position and orientation to another chosen configuration within the hand workspace.
That definition covers in-hand rotation, but current usage is often broader. Regrasping, finger gaiting, sliding, rolling, tool use and precise insertion can all be called dexterous when they require controlled contact changes. In-hand manipulation is therefore an important subset, not a synonym for every dexterous task.
A dexterous hand does not guarantee dexterous behaviour
Many actuated joints can increase the available motions, but hardware complexity alone does not establish dexterity. Actuation, sensing, controllability, compliance, friction, calibration and the policy all affect what the system can perform reliably.
The field has no universal degree-of-freedom threshold. Recent survey work compares hands under different embodiments, sensory configurations and evaluation protocols, which makes the claimed capability and test task more informative than the label “dexterous hand”.
Contact-rich data carries the missing detail
Vision can record an object's gross motion while hiding fingertip pressure, incipient slip and changes in contact mode. Dexterous datasets benefit from synchronised hand state, commanded and measured motion, tactile or force signals, object pose, contact annotations and failures.
The hand model and control interface also matter. A demonstration collected with one tendon routing, fingertip geometry or action space may not transfer directly to another hand. Dataset documentation should separate task-level dexterity from embodiment-specific commands and report success over repeated physical trials.
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Related terms
Hardware & control
Robot manipulation
Robot manipulation is a robot's controlled physical interaction with objects or its environment to change or maintain their state. It includes grasping, carrying, pushing, pulling, inserting, wiping, folding, tool use, and other tasks performed through selective contact. Manipulation can use a gripper, hand, tool, arm, or another part of the robot.
Hardware & control
Gripper
A gripper is a robot end effector designed to seize and hold an object. It may use fingers, jaws, suction, magnetism, adhesion, or another grasping mechanism. A gripper can open and close with one command or expose several independently controlled joints, but it is not synonymous with every end effector or with a complete robot hand.
Hardware & control
Tactile sensing
Tactile sensing is the detection and measurement of physical contact properties at a robot's surface or contact interface. Depending on the sensor, it can report pressure or force distribution, contact location, shear, vibration, slip, texture, temperature, or deformation. Tactile data complements vision by measuring interactions that may be hidden at the point of contact.
Hardware & control
End effector
An end effector is a task-specific device attached to a robot manipulator’s mechanical interface so the robot can act on its environment, such as a gripper, hand or welding tool. It is distinct from the wrist or mounting flange, and from the tool centre point, which is only a coordinate frame used to plan the device’s motion.
Data & collection
Teleoperation
Teleoperation is real-time human control of a robot from a remote or mediated interface. For humanoid training-data collection, the operator’s inputs are mapped to robot motions while cameras, proprioception, commands and outcomes are recorded, producing embodied demonstrations in the robot’s own observation and action spaces.