SlashML Robotics Python SDK

Ultra-low-latency spatial memory and dynamic belief tracking for embodied AI, mobile manipulation, and autonomous robots.

Overview

slashml-robotics provides a sub-millisecond, zero-recomputation spatial memory substrate for robotics perception pipelines and mission planners:

• O(1) Dynamic Spatial Supersession: Updates object locations instantly and marks obsolete historical coordinates as superseded without full-database reindexing.
• Sub-Microsecond where_is: Query active 6-DoF poses and room zones with sub-microsecond retrieval latency.
• Chronological Trajectory Reconstruction: Full movement history tracking across workspaces and rooms.
• Physical Affordance Search: Discover tools and objects based on functional capabilities (e.g. fastening, measuring, stripping).

Installation

pip install slashml-robotics

Quickstart

from slashml_robotics import RoboticMemory, Pose6D

# Initialize client connected to SlashML engine daemon or cloud endpoint
memory = RoboticMemory(base_url="http://localhost:8000", api_key="sk_...")

# 1. Ingest camera / perception detection
memory.add_observation(
    object_name="torque_wrench",
    room_zone="machining_bay",
    pose=Pose6D(x=1.24, y=-0.52, z=0.85, qx=0.0, qy=0.0, qz=0.0, qw=1.0),
    tags=["tool", "hardware"],
)

# 2. Query active location
loc = memory.where_is("torque_wrench")
if loc:
    print(f"Found {loc.object_name} in {loc.room_zone} at ({loc.pose.x}, {loc.pose.y}, {loc.pose.z})")

# 3. Retrieve movement trajectory
trajectory = memory.get_trajectory("torque_wrench")
print(f"Total waypoints recorded: {trajectory.total_waypoints}")

# 4. Search by physical affordance
tools = memory.find_by_affordance("fastening", room_zone="machining_bay")
for t in tools:
    print(f"Available fastening tool: {t.object_name}")

License

Apache-2.0