VIP-Loco: A Visually Guided Infinite Horizon Planning Framework for Legged Locomotion

• URL: http://arxiv.org/abs/2603.14345v1
• Date: Sun, 15 Mar 2026 12:29:53 GMT
• Title: VIP-Loco: A Visually Guided Infinite Horizon Planning Framework for Legged Locomotion
• Abstract summary: VIP-Loco is a framework that integrates vision-based scene understanding with RL and planning.<n>We show that VIP-Loco unifies planning and perception, enabling robust, interpretable locomotion in diverse environments.
• Score: 2.85987101794478
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Perceptive locomotion for legged robots requires anticipating and adapting to complex, dynamic environments. Model Predictive Control (MPC) serves as a strong baseline, providing interpretable motion planning with constraint enforcement, but struggles with high-dimensional perceptual inputs and rapidly changing terrain. In contrast, model-free Reinforcement Learning (RL) adapts well across visually challenging scenarios but lacks planning. To bridge this gap, we propose VIP-Loco, a framework that integrates vision-based scene understanding with RL and planning. During training, an internal model maps proprioceptive states and depth images into compact kinodynamic features used by the RL policy. At deployment, the learned models are used within an infinite-horizon MPC formulation, combining adaptability with structured planning. We validate VIP-Loco in simulation on challenging locomotion tasks, including slopes, stairs, crawling, tilting, gap jumping, and climbing, across three robot morphologies: a quadruped (Unitree Go1), a biped (Cassie), and a wheeled-biped (TronA1-W). Through ablations and comparisons with state-of-the-art methods, we show that VIP-Loco unifies planning and perception, enabling robust, interpretable locomotion in diverse environments.

Related papers

• MAPLE: Latent Multi-Agent Play for End-to-End Autonomous Driving [62.43744546817599]
  Vision-language--action (VLA) models are effective as end-to-end motion planners, but can be brittle when evaluated in closed-loop settings.<n>We propose MAPLE, a novel framework for reactive, multi-agent rollout of a dynamic driving scenario in the latent space of the VLA model.<n> MAPLE achieves state-of-the-art driving performance on Bench2Drive and demonstrates scalable, closed-loop multi-agent play for robust E2E autonomous driving systems.
  arXiv  Detail & Related papers  (2026-05-13T23:35:14Z)
• GS-Playground: A High-Throughput Photorealistic Simulator for Vision-Informed Robot Learning [55.272458304179025]
  GS-Playground is a multi-modal simulation framework designed to accelerate end-to-end perceptual learning.<n>Our system achieves a breakthrough throughput of 104 FPS at 640x480 resolution, significantly lowering the barrier for large-scale visual RL.
  arXiv  Detail & Related papers  (2026-04-28T10:05:39Z)
• SGDrive: Scene-to-Goal Hierarchical World Cognition for Autonomous Driving [52.02379432801349]
  We propose SGDrive, a novel framework that structures the VLM's representation learning around driving-specific knowledge hierarchies.<n>Built upon a pre-trained VLM backbone, SGDrive decomposes driving understanding into a scene-agent-goal hierarchy that mirrors human driving cognition.
  arXiv  Detail & Related papers  (2026-01-09T08:55:42Z)
• VLMPlanner: Integrating Visual Language Models with Motion Planning [18.633637485218802]
  VLMPlanner is a hybrid framework that combines a learning-based real-time planner with a vision-language model (VLM) capable of reasoning over raw images.<n>We develop the Context-Adaptive Inference Gate mechanism that enables the VLM to mimic human driving behavior.
  arXiv  Detail & Related papers  (2025-07-27T16:15:21Z)
• Monte Carlo Tree Search with Velocity Obstacles for safe and efficient motion planning in dynamic environments [49.30744329170107]
  We propose a novel approach for optimal online motion planning with minimal information about dynamic obstacles.<n>The proposed methodology combines Monte Carlo Tree Search (MCTS), for online optimal planning via model simulations, with Velocity Obstacles (VO), for obstacle avoidance.<n>We show the superiority of our methodology with respect to state-of-the-art planners, including Non-linear Model Predictive Control (NMPC), in terms of improved collision rate, computational and task performance.
  arXiv  Detail & Related papers  (2025-01-16T16:45:08Z)
• PIP-Loco: A Proprioceptive Infinite Horizon Planning Framework for Quadrupedal Robot Locomotion [1.123472110161393]
  A core strength of Model Predictive Control (MPC) for quadrupedal locomotion has been its ability to enforce constraints.<n>We propose a framework that integrates proprioceptive planning with Reinforcement Learning (RL)<n>During deployment, the Dreamer module solves an infinite-horizon MPC problem, adapting actions and velocity commands to respect the constraints.
  arXiv  Detail & Related papers  (2024-09-14T13:51:37Z)
• PlaMo: Plan and Move in Rich 3D Physical Environments [68.75982381673869]
  We present PlaMo, a scene-aware path planner and a robust physics-based controller. The planner produces a sequence of motion paths, considering the various limitations the scene imposes on the motion. Our control policy generates rich and realistic physical motion adhering to the plan.
  arXiv  Detail & Related papers  (2024-06-26T10:41:07Z)
• Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Control [106.32794844077534]
  This paper presents a study on using deep reinforcement learning to create dynamic locomotion controllers for bipedal robots. We develop a general control solution that can be used for a range of dynamic bipedal skills, from periodic walking and running to aperiodic jumping and standing. This work pushes the limits of agility for bipedal robots through extensive real-world experiments.
  arXiv  Detail & Related papers  (2024-01-30T10:48:43Z)
• Learning to Jump from Pixels [23.17535989519855]
  We present Depth-based Impulse Control (DIC), a method for synthesizing highly agile visually-guided behaviors. DIC affords the flexibility of model-free learning but regularizes behavior through explicit model-based optimization of ground reaction forces. We evaluate the proposed method both in simulation and in the real world.
  arXiv  Detail & Related papers  (2021-10-28T17:53:06Z)
• An Adaptable Approach to Learn Realistic Legged Locomotion without Examples [38.81854337592694]
  This work proposes a generic approach for ensuring realism in locomotion by guiding the learning process with the spring-loaded inverted pendulum model as a reference. We present experimental results showing that even in a model-free setup, the learned policies can generate realistic and energy-efficient locomotion gaits for a bipedal and a quadrupedal robot.
  arXiv  Detail & Related papers  (2021-10-28T10:14:47Z)
• RLOC: Terrain-Aware Legged Locomotion using Reinforcement Learning and Optimal Control [6.669503016190925]
  We present a unified model-based and data-driven approach for quadrupedal planning and control. We map sensory information and desired base velocity commands into footstep plans using a reinforcement learning policy. We train and evaluate our framework on a complex quadrupedal system, ANYmal B, and demonstrate transferability to a larger and heavier robot, ANYmal C, without requiring retraining.
  arXiv  Detail & Related papers  (2020-12-05T18:30:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.