Workflow:Google deepmind Dm control Manipulation Task Setup
| Knowledge Sources | |
|---|---|
| Domains | Reinforcement_Learning, Manipulation, Robotics |
| Last Updated | 2026-02-15 12:00 GMT |
Overview
End-to-end process for loading and running robotic manipulation tasks using the dm_control manipulation suite, featuring a Kinova Jaco arm performing reach, lift, place, and brick assembly tasks.
Description
This workflow covers the standard procedure for using the dm_control manipulation task suite. The suite provides a structured set of manipulation tasks centered on a Kinova Jaco robotic arm and hand operating in a shared arena. Tasks range from simple reaching (moving the hand to a target) through lifting (raising objects to a target height) and placing (positioning objects on targets) to complex brick assembly (stacking Duplo bricks in order). Each task uses Composer entities (JacoArm, JacoHand, Duplo bricks, primitive props) with standardized observation categories (proprioception, force/torque, camera views). The output is a dm_env-compatible environment for training manipulation policies.
Usage
Execute this workflow when you need a standardized robotic manipulation benchmark environment. The manipulation suite is suitable for evaluating visuomotor control, dexterous manipulation, and object interaction algorithms using a physically realistic robot arm simulation.
Execution Steps
Step 1: Discover Available Manipulation Tasks
Query the manipulation module's global registry to discover available task names and their tags. Tasks are registered with metadata tags indicating difficulty and category. The registry provides functions to list all task names, filter by tag, and retrieve task constructors by name.
Key considerations:
- Task names follow the pattern: {task_type}_{prop_type}_{observation_type}
- Tags categorize tasks by type (e.g., vision, features) and difficulty
- Available task categories: reach, lift, place, reassemble, stack
- Each task has variants with different observation configurations
Step 2: Load a Manipulation Environment
Use the manipulation.load() function to instantiate an environment by name. The loader retrieves the task constructor from the registry, instantiates the task (which builds the robot, props, and arena), and wraps it in a composer.Environment with a 10-second time limit. An optional seed parameter controls the random state for reproducibility.
Key considerations:
- The load function accepts environment_name and optional seed
- Time limit defaults to 10 seconds per episode
- The timeout flag can be set to False for unlimited episodes
- Each task constructor builds the full Composer entity hierarchy internally
Step 3: Understand the Robot Configuration
The manipulation suite uses a Kinova Jaco arm with 6 degrees of freedom and a three-finger gripper hand. The arm uses velocity actuators with torque sensors; the hand has configurable finger coupling modes. The robot is constructed from MJCF XML assets with pre-calibrated physical properties. Understanding the actuator layout is essential for designing control policies.
Key considerations:
- JacoArm provides 6-DOF velocity control with torque feedback
- JacoHand has three fingers with velocity-controlled actuators
- The tool center point (TCP) is the reference frame for end-effector tasks
- Inverse kinematics (IK) is available for TCP-based initialization
- Robot observables include joint positions, velocities, and torque sensors
Step 4: Understand the Task Reward Structure
Each manipulation task defines a specific reward function based on the task objective. Reach tasks reward proximity of the TCP to a target position. Lift tasks reward raising a prop above a height threshold. Place tasks reward positioning a prop on a target location. Brick tasks reward correct assembly ordering and stability. Rewards use soft tolerance functions that provide smooth gradients.
Key considerations:
- Reach tasks use distance-based rewards between TCP and target
- Lift tasks reward vertical displacement of the grasped object
- Place tasks combine grasp reward with placement accuracy reward
- Brick assembly tasks verify correct ordering and stable stacking
- Reward functions use dm_control.utils.rewards.tolerance for smooth shaping
Step 5: Run the Manipulation Episode Loop
Execute the standard dm_env interaction loop: reset, then repeatedly step with actions until episode termination. Actions are continuous numpy arrays controlling joint velocities. Observations include proprioceptive state (joint angles, velocities), force/torque readings, and optionally camera images for vision-based tasks.
Key considerations:
- Actions control joint velocities within specified bounds
- Observations are grouped by category: proprioception, touch, visual
- The arena provides standardized camera viewpoints (front_close, top, left_close, right_close)
- Workspaces define spatial bounds for robot and prop initialization
- Episode initialization uses collision-aware placement for props and TCP
Step 6: Visualize the Manipulation Environment
Use the manipulation/explore.py script or dm_control.viewer to interactively visualize manipulation tasks. The viewer enables inspection of the robot configuration, prop placement, camera views, and reward signals during manual or policy-driven interaction.
Key considerations:
- manipulation/explore.py accepts --environment flag to select a task
- Multiple camera views are available for different perspectives
- The viewer supports body perturbation for testing grasp robustness
- Depth buffer visualization can be enabled for depth-based observation debugging