**Adaptive Multimodal Control in Dynamic Environments**

Adaptive Multimodal Control in Dynamic Environments

Consider a scenario where a robotic agent must interact with a dynamic environment comprising multiple modes of control and sensorimotor feedback. The objective is to develop an adaptive reinforcement learning algorithm that learns to adjust its control policy and sensorimotor mappings to minimize performance gaps across different operating conditions and control modes.

Constraints:

• The robotic agent operates in a 3D environment with six degrees of freedom (DOF), consisting of a robotic arm and a wheeled base.
• The agent interacts with a multimodal world where it can engage with different objects and sensors, yielding diverse sensorimotor feedback streams.
• The control modes include:
  • Precision mode: Minimize joint movement to achieve high-precision object manipulation.
  • Efficiency mode: Maximize joint movement to achieve high-speed object transportation.
  • Exploration mode: Randomize joint movement to gather knowledge about the environment.
• The operating conditions include:
  • Indoor environment with varying lighting conditions and object types.
  • Outdoor environment with changing weather conditions and terrain.
• The sensorimotor mappings include:
  • Joint position and velocity measurements.
  • Force and torque sensors for object interaction.
  • Visual and auditory sensors for environmental awareness.
• The performance metrics include:
  • Task completion time.
  • Object manipulation accuracy.
  • Energy consumption.

Requirements:

1. Develop an adaptive reinforcement learning algorithm that seamlessly transitions between precision, efficiency, and exploration modes based on changing operating conditions and performance metrics.
2. Implement a dynamic sensorimotor mapping mechanism that learns to adapt to various sensor readings and control modes.
3. Design a multimodal control framework that integrates joint motion planning, object manipulation control, and environmental interaction.
4. Evaluate the performance of the algorithm using a variety of indoor and outdoor scenarios with diverse lighting, weather, and terrain conditions.

Evaluation criteria:

• Performance improvements over a baseline algorithm (e.g., 5% reduction in task completion time).
• Adaptability to changing operating conditions and control modes.
• Efficiency and precision in object manipulation and transportation.
• Robustness to sensor noise and environmental variability.

Submission guidelines:

• Submit a detailed algorithm description, including theoretical foundations, architecture, and implementation details.
• Provide experimental results, including dataset collection, algorithm training, and evaluation protocols.
• Offer a discussion of the algorithm's strengths, limitations, and potential applications.

Deadline: March 15, 2026

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