tbp.monty.simulators#

tbp.monty.simulators.simulator#

class Simulator(*args, **kwargs)[source]#

Bases: Protocol

A Protocol defining a simulator for use in simulated environments.

A Simulator is responsible for a simulated environment that contains objects to interact with, agents to do the interacting, and for collecting observations and proprioceptive state to send to Monty.

add_object(name: str, position: Tuple[float, float, float] = (0.0, 0.0, 0.0), rotation: Tuple[float, float, float, float] = (1.0, 0.0, 0.0, 0.0), scale: Tuple[float, float, float] = (1.0, 1.0, 1.0), semantic_id: str | None = None, enable_physics=False, object_to_avoid=False, primary_target_bb: List | None = None) → None[source]#

Add new object to simulated environment.

Adds a new object based on the named object. This assumes that the set of available objects are preloaded and keyed by name.

Parameters:

name (str) – Registered object name
position (VectorXYZ) – Initial absolute position of the object
rotation (QuaternionWXYZ) – Initial orientation of the object
scale (VectorXYZ) – Initial object scale
semantic_id (Optional[str]) – Optional override object semantic ID
enable_physics (bool) – Whether to enable physics on the object
object_to_avoid (bool) – If True, ensure the object is not colliding with other objects
primary_target_bb (List | None) – If not None, this is a list of the min and max corners of a bounding box for the primary object, used to prevent obscuring the primary objet with the new object.

apply_action(action: Action) → Dict[str, Dict][source]#

Execute the given action in the environment.

Parameters:: action (Action) – the action to execute
Returns:: A dictionary with the observations grouped by agent_id
Return type:: (Dict[str, Dict])

close()[source]#: Close any resources used by the simulator.

get_action_space()[source]#: Returns the set of all available actions.

get_agent(agent_id)[source]#: Return agent instance.

get_num_objects() → int[source]#: Return the number of instantiated objects in the environment.

get_observations()[source]#: Get sensor observations.

get_states()[source]#: Get agent and sensor states.

initialize_agent(agent_id, agent_state)[source]#: Update agent runtime state.

remove_all_objects()[source]#: Remove all objects from the simulated environment.

reset()[source]#: Reset the simulator.