Mobile Autonomous Systems Laboratory¶

Course Overview¶

• Institution: MIT
• Course code: 6.186
• Track: Robotics and Autonomous Systems
• Tier: B
• Role: Supplement
• Level: Not standardized by provider (use prerequisites)
• Last reviewed: 2026-07-28

MIT's Mobile Autonomous Systems Laboratory preserves useful project organization and validation methods for mobile autonomy, but its obsolete 2005 platform limits the material to methodological reference.

Why choose this course

Supplement course. Useful for specific topics and best paired with a more complete mainline resource.

Before you start

• Recommended foundation: Control Systems
• Recommended foundation: Programming and Engineering Computing
• Recommended foundation: Physics Foundations

Verifiable learning outcomes

• Explain the core models in Robotics and Autonomous Systems, including their assumptions and limits
• Complete a reproducible experiment or implementation with raw data, parameters, versions, and verification

Workload and pacing

7 weeks at 6 hours/week. This maintainer planning estimate is derived from course role and the density of public practice and labs; it is not a provider workload promise. Pilot two weeks while logging instruction, practice, lab, and review time, then adjust the remaining plan when actual effort differs by more than 25%.

Safety level

Simulation only. The default practice scope is software, computation, or simulation only; a lab label in the resource inventory does not authorize connecting physical equipment, and any hardware extension requires provider-scope verification and a new risk assessment.

Course Resources¶

Software, hardware, and cost

Software

• Maintainer-suggested open-source/free verification path: ROS 2, Gazebo, RViz 2, Python or C++, and a version-pinned container environment
• The resource inventory lists public code coverage; pin interpreter, dependencies, toolchain, datasets, and PDK versions where applicable

Hardware

• The resource inventory lists lab coverage, but this course's maintainer path explicitly limits it to computational or simulation work. It assumes only a general-purpose computer able to run the software above and retain results; do not purchase or connect a course-supported robot platform, sensors, low-voltage power, emergency stop, and safe test area

Cost note

The current maintainer path uses computation and simulation only, with no dedicated hardware purchase, and prefers open-source/free tools. This is not a provider requirement; platform, commercial-software, or cloud-compute costs still vary by provider, region, and plan.

Public resource coverage

Resources and access

“Listed by official page” means the link was discovered on a successfully fetched official source on the verification date; it does not guarantee that every region or account can open the target directly. Access does not grant redistribution rights. Re-check the provider page, target link, and third-party notices before downloading, adapting, or publishing material.

Practice and Verification¶

Practice loop

Mobile Autonomous Systems Laboratory · MIT 6.186: Robot Task Planning and Safe-Degradation Simulation

This is a maintainer-suggested self-study project for Mobile Autonomous Systems Laboratory · MIT 6.186, not an official course assignment. Complete a perception–planning–control task in simulation for Robotics and Autonomous Systems, quantifying success rate, collision margin, localization error, and safe stop after sensor failure.

Origin: Maintainer-suggested project

Deliverables

• A specification of task, robot and environment models, frames, constraints, and safe state
• Perception, planning, control, monitoring, and scenario-generation sources
• Raw trajectories, success or collision labels, minimum clearance, and runtime for at least 100 randomized scenes
• A report and screen recording comparing baseline and improved methods and reviewing the most hazardous failure

Verification

• Achieve at least 90% success over 100 nominal scenes with zero collisions and the predeclared minimum clearance
• Cover coincident start and goal, infeasible maps, narrow passages, localization drift, and sensor interruption
• Replay every trajectory through an independent collision checker and cross-check frame by frame
• Inject frozen sensing or control delay and show the monitor reaches a stopped state within the specified time

Reproducibility

• Commit robot and world models, algorithms, scenarios, tests, and recording scripts
• Pin simulator, physics step, maps, random seeds, and dependency versions
• Preserve raw trajectories and sensor data, scenario manifests, and the generated report

Safety boundary: Simulation only — Use robot simulation only; do not drive real mechanisms, vehicles, drones, or actuators without qualified supervision.

Risks, gaps, and boundaries

The 2005 mobile platform is obsolete; use this only as a project-method reference.

Completion evidence

• Weekly learning log with time, questions, corrected errors, decisions, next steps, and links to that week's reproducible artifacts
• Design-review package with requirements and constraints, trade-offs, editable sources, applicable ERC/DRC/timing/stability checks, exports, and a reproduction test
• Code repository with pinned dependencies and toolchain, a minimal run command, tests or waveform/benchmark checks, expected output, and license notes
• Simulation package with model or netlist, inputs, solver and version, parameter-sweep script, benchmark comparison, expected results, and one rerun command