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arXiv This work presents a language-guided terrain-adaptive neural model predictive control (MPC) framework for autonomous traversal of articulated tracked robots (ATRs) in contact-rich environments such as stairwells and cluttered building interiors: a terrain-conditioned neural kinematics model predicts short-horizon task-state increments from a local height sequence and recent trajectories, neural MPC plans with multi-objective costs and strict feasibility constraints, and a large language model (LLM) enables terrain adaptation by proposing bounded updates to selected weights and bounds through a safety-checked interface with range clipping, rate limiting, and consistency checks; the compiled predictor enables a full control cycle within 100 ms, and across three traversal tasks and a multi-he
This work presents a language-guided terrain-adaptive neural model predictive control (MPC) framework for autonomous traversal of articulated tracked robots (ATRs) in contact-rich environments such as stairwells and cluttered building interiors: a terrain-conditioned neural kinematics model predicts short-horizon task-state increments from a local height sequence and recent trajectories, neural MPC plans with multi-objective costs and strict feasibility constraints, and a large language model (LLM) enables terrain adaptation by proposing bounded updates to selected weights and bounds through a safety-checked interface with range clipping, rate limiting, and consistency checks; the compiled predictor enables a full control cycle within 100 ms, and across three traversal tasks and a multi-he
This work presents a language-guided terrain-adaptive neural model predictive control (MPC) framework for autonomous traversal of articulated tracked robots (ATRs) in contact-rich environments such as stairwells and cluttered building interiors: a terrain-conditioned neural kinematics model predicts short-horizon task-state increments from a local height sequence and recent trajectories, neural MPC plans with multi-objective costs and strict feasibility constraints, and a large language model (LLM) enables terrain adaptation by proposing bounded updates to selected weights and bounds through a safety-checked interface with range clipping, rate limiting, and consistency checks; the compiled predictor enables a full control cycle within 100 ms, and across three traversal tasks and a multi-he
This work presents a language-guided terrain-adaptive neural model predictive control (MPC) framework for autonomous traversal of articulated tracked robots (ATRs) in contact-rich environments such as stairwells and cluttered building interiors: a terrain-conditioned neural kinematics model predicts short-horizon task-state increments from a local height sequence and recent trajectories, neural MPC plans with multi-objective costs and strict feasibility constraints, and a large language model (LLM) enables terrain adaptation by proposing bounded updates to selected weights and bounds through a safety-checked interface with range clipping, rate limiting, and consistency checks; the compiled predictor enables a full control cycle within 100 ms, and across three traversal tasks and a multi-he