Skip to main content

AI Assistant

Physical AI & Humanoid Robotics

Hello! I'm your AI assistant for the AI-Native Guide to Physical AI & Humanoid Robotics. How can I help you today?

04:57 AM

Topic 3 — Task Allocation, Role Assignment & Distributed Planning

Assigning the right tasks to the right robots is a cornerstone of effective multi-agent systems. This topic presents core strategies for distributing work among a team, planning as a group, and ensuring robust execution even as the environment and available agents change.

3.1 Assignment Models​

How do robots decide who does what?

Auction-Based Allocation​

  • Robots broadcast available tasks and bid based on cost (distance, time, capability).
  • Highest/lowest bid wins, all robots update their assignments accordingly.
  • Useful for decentralized, dynamic teams.

Leader Election​

  • One robot (elected via protocol, e.g. Bully algorithm) organizes and hands out tasks.
  • Only used when centralization is temporarily advantageous or failover is needed.
  • Robustness: If the leader fails, another is elected.

Graph Partitioning/Splitting​

  • Map divided into regions/subtasks, assigned by dividing a graphical representation (e.g., Voronoi partitioning, nearest neighbor, coverage maps).
  • Great for spatially-structured missions.

Diagram: Auction-Based Task Allocation Flow​

          +----------------+
          |  Task Request  |
          +--------+-------+
                   |
           Robots submit Bids
                   |
          +--------v--------+
          |  Auctioneer     |
          +--------+--------+
                   |
       Assign task to winning robot
                   |
          +--------v--------+
          |   Winner does   |
          |   task, others  |
          |   re-bid/remain |
          +-----------------+

3.2 Planning as a Group​

Negotiation and fallback

  • Robots can negotiate ownership ("I'm closest, I'll do it!") or request help.
  • If a robot becomes incapacitated, tasks are dynamically transferred.
  • Subtasks may have dependencies: robots must communicate about what must happen first and next.

Protocols for distributed planning

  • Often implemented as distributed consensus (task logs, distributed locks), or lighter-weight, decentralized heuristics (assignment based on most recent/frequent user, etc.).

3.3 Load Balancing & Efficiency​

  • Shortest path assignment: Minimize travel/time across the team.
  • Energy vs time trade-off: Decide whether to balance robot battery use or minimize total mission time.
  • Dynamic redistribution: If a robot's progress stalls, incomplete tasks are re-broadcast and reassigned.

Lab: Simulated Auction-Based Distributed Task Assignment​

Goal: Simulate a fleet of robots negotiating task assignment in simulation using auction/market protocols.

Tasks:​

  1. Set up 2+ robots in Gazebo/Isaac Sim, each with a basic task queue (mock delivery/inspection/search jobs).
  2. Implement a simple auctioneer node (central or rotating):
    • Announces new jobs.
    • Robots compute bids (distance to site, time, or random weight for demonstration).
    • Auctioneer assigns job to best bidder; others rebid for remaining jobs.
  3. Introduce dropout/failure: one robot drops out; observe that tasks are re-auctioned and assigned to remaining robots.
  4. Log:
    • Assignment sequence.
    • Bids per robot.
    • Task completion vs dropped/failed tasks.

Deliverables:​

  • Auction/election node code and launch configs.
  • Task queue/bidding logic logs or visualization.
  • Short report on efficiency, robustness, and recovery behavior.

Summary​

Effective distributed task allocation and planning are what allow multi-robot teams to be more than "just many single robots." These algorithms enable scale, resilience, and workload optimization—fueling mission performance in warehouses, search/rescue, and dynamic human-robot teaming.