Sample-efficient Low-level Motion Planning for Robotic Manipulation Tasks via Zero-shot Transfer Learning
Proposes iCEM+TL framework integrating transfer learning to boost low-level robotic motion planning success rate by 23%, enabling zero-shot transfer for complex tasks.
Key Findings
Methodology
This paper introduces an iCEM+TL framework that embeds transfer learning (TL) into the Cross-Entropy Method (CEM) for robotic control. The approach involves transferring key parameters—mean μ and standard deviation σ—of the Gaussian sampling distribution from simpler upstream tasks to guide complex downstream tasks. Task decomposition is employed to break down complex objectives into sub-goals, with reward functions redesigned to emphasize task-specific cues such as object distances, gripper position, and stacking height. Elite trajectories from upstream tasks are incorporated at each planning step to enhance exploration. The framework operates in MuJoCo simulation environments (FetchStack, FetchSlide, Shelf) and is validated on a real Franka Emika robot, demonstrating success rate improvements up to 23%. The method emphasizes knowledge reuse, structured exploration, and reward shaping, leading to more efficient and robust low-level control.
Key Results
- In simulation, the FetchStack task showed a 23% increase in success rate over baseline iCEM, outperforming deep RL methods like TQC+HER and generative imitation models like PointFlowMatch, especially in long-horizon, multi-object stacking scenarios.
- Transfer effectiveness varies with task similarity; high structural alignment between upstream and downstream tasks correlates with larger improvements, validating the proposed task-matching criterion.
- On real hardware, the transferred trajectories from simulation enabled the robot to successfully perform stacking tasks, confirming the practical applicability of the approach.
Significance
This work addresses the critical challenge of high sample complexity and long training times in robotic manipulation, especially for complex, multi-step tasks. By integrating transfer learning directly into a low-level optimizer, it offers a zero-shot solution that leverages prior knowledge without extensive retraining. The approach enhances the efficiency, robustness, and generalization of robotic control, making it highly relevant for industrial automation, service robots, and adaptive manufacturing. The task structure matching metric provides a new theoretical tool for guiding transfer decisions, bridging the gap between simulation and real-world deployment.
Technical Contribution
The primary technical innovation is embedding transfer learning mechanisms into the iCEM algorithm, enabling the reuse of sampling distributions and elite trajectories across tasks. The integration of task decomposition and reward redesign further refines the optimization process, addressing sparse rewards and long-term dependencies. The introduction of a task structure matching criterion allows for effective selection of upstream tasks, ensuring positive transfer. Extensive experiments in simulation and on physical robots demonstrate improved success rates and robustness, establishing a new paradigm for low-level motion planning that combines optimization, knowledge reuse, and structured task analysis.
Novelty
This research is the first to embed transfer learning directly into an evolutionary optimization framework for low-level robotic control, bypassing the need for large neural policies or offline training. Unlike prior works that focus on high-level policy transfer or neural network-based imitation, this method emphasizes parameter transfer within a model-based planner, guided by task decomposition and reward shaping. The explicit task structure matching metric further distinguishes this work, providing a quantifiable measure for transfer effectiveness. This combination of techniques offers a novel, interpretable, and computationally efficient pathway for complex manipulation tasks.
Limitations
- The effectiveness of transfer heavily depends on the structural similarity between upstream and downstream tasks; significant differences can lead to ineffective or negative transfer, limiting generalization.
- In highly dynamic or unpredictable environments, the static transfer parameters may become outdated, requiring online adaptation mechanisms for robustness.
- Current validation is limited to simulation and a single real-world task; broader testing across diverse scenarios and tasks is necessary to confirm generalization.
Future Work
Future directions include integrating high-level learned policies with low-level optimization for hierarchical planning, developing adaptive transfer strategies that dynamically select suitable upstream tasks, and extending validation to more complex, contact-rich, and orientation-constrained environments. Additionally, exploring online parameter adjustment and meta-learning approaches could further enhance robustness and transferability in real-world applications.
AI Executive Summary
Robotic manipulation tasks are becoming increasingly complex, involving multi-object stacking, sliding, and shelf placement, which challenge existing low-level motion planners. Traditional approaches like deep reinforcement learning (DRL) achieve reliable performance but demand extensive training data and computational resources, limiting their practicality for real-time applications. Evolutionary algorithms such as the Cross-Entropy Method (CEM) and its improved variant iCEM offer gradient-free optimization suitable for high-dimensional control but struggle with sample efficiency and long-horizon tasks.
In response to these limitations, this paper introduces an innovative framework called iCEM+TL, which integrates transfer learning (TL) into the iCEM algorithm. The core idea is to transfer key parameters—mean μ and standard deviation σ—from simpler upstream tasks to guide the optimization process in more complex downstream tasks. This transfer is complemented by task decomposition strategies that break down complex objectives into manageable sub-goals, and reward redesign that emphasizes task-specific cues such as object distances, gripper positions, and stacking heights.
The framework operates by initializing the sampling distribution with transferred parameters, incorporating elite trajectories from upstream tasks at each planning step, and iteratively refining the sampling distribution based on cumulative rewards. This process significantly accelerates convergence and improves success rates. Extensive experiments in MuJoCo simulation environments demonstrate success rate improvements of up to 23% across tasks like FetchStack, FetchSlide, and Shelf. The approach outperforms baseline methods, including deep RL algorithms and generative imitation models, especially in long-horizon, multi-object scenarios.
To validate real-world applicability, the authors deployed the method on a Franka Emika robot, where the transferred trajectories from simulation enabled successful stacking operations. This confirms the practical feasibility of the approach, highlighting its potential for industrial automation and service robotics.
The key contributions include the novel integration of transfer learning into low-level motion optimization, a structured task decomposition and reward shaping methodology, and a task structure matching criterion that guides effective transfer. These innovations collectively address the sample inefficiency, long-horizon planning, and generalization challenges faced by existing methods. The research opens new avenues for developing autonomous robots capable of efficiently adapting to complex, real-world tasks without extensive retraining, thus bridging the gap between simulation and deployment.
Deep Analysis
Background
Robotic manipulation has evolved from simple pick-and-place tasks to complex multi-object operations, driven by advances in sensors, actuators, and algorithms. Early methods relied on classical motion planning algorithms like RRT and A*, which excel in structured environments but lack adaptability. The advent of deep reinforcement learning (DRL) introduced end-to-end learning of control policies, exemplified by algorithms such as DDPG, SAC, and TQC, enabling robots to learn from interaction data. However, DRL methods require massive datasets and long training times, especially for tasks involving sparse rewards and long horizons.
To mitigate these issues, researchers turned to model-based optimization algorithms like CEM and iCEM, which perform gradient-free search in action space, offering better sample efficiency and interpretability. Concurrently, transfer learning (TL) gained prominence as a means to reuse knowledge across tasks, reducing training costs and improving generalization. Techniques such as goal-conditioned policies and hierarchical learning have been explored, but their integration with low-level motion planning remains limited.
Despite these advancements, challenges persist in scaling to complex, multi-step tasks with high-dimensional state-action spaces. Sparse rewards and long-term dependencies hinder exploration and convergence. Moreover, transfer effectiveness varies significantly with task similarity, necessitating mechanisms to quantify and optimize transfer pathways. This context motivates the development of a unified framework that combines the strengths of evolutionary optimization, transfer learning, and task decomposition to address these bottlenecks.
Core Problem
The core problem addressed in this work is how to efficiently perform low-level motion planning for complex robotic manipulation tasks without relying on extensive offline training or neural policies. Existing methods either demand large datasets (DRL) or suffer from poor sample efficiency and limited scalability (traditional optimization). While transfer learning offers a promising avenue to accelerate learning by leveraging prior knowledge, its application in low-level control remains underexplored, especially in a way that guarantees positive transfer.
Furthermore, complex tasks such as stacking or shelf placement involve sparse rewards, long horizons, and intricate contact dynamics, making naive optimization or direct transfer ineffective. The challenge is to develop a method that can effectively reuse knowledge from simpler tasks, guide exploration in high-dimensional spaces, and handle task-specific nuances through reward shaping and task decomposition. Achieving this would significantly reduce training time, improve success rates, and enable robots to adapt quickly to new, complex scenarios in real-world settings.
Innovation
This paper introduces several key innovations:
- �� Transfer of key parameters (μ and σ) from simpler tasks to initialize the sampling distribution in iCEM, enabling zero-shot guidance for complex tasks.
- �� Task decomposition strategies that break down complex objectives into sub-goals, facilitating structured reward design and more targeted exploration.
- �� A novel task structure matching metric that quantifies the similarity between upstream and downstream tasks, guiding the selection of effective transfer sources.
- �� Integration of elite trajectory transfer at each planning step, combining prior knowledge with local exploration for improved convergence.
- �� Validation on both simulation and real hardware, demonstrating the method’s robustness and practical relevance.
These innovations collectively enable a more efficient, interpretable, and scalable approach to low-level motion planning, addressing the limitations of existing methods in handling complex, multi-object tasks.
Methodology
- �� 任务定义：将每个任务描述为对象集O、初始状态s0和目标状态g，目标是找到动作序列a0:T，使对象从s0移动到g，最大化累计奖励。• 迁移参数：在每个时间步t，将上游任务中学到的高斯分布参数μ和σ迁移到当前任务中，作为采样的起点。• elite轨迹迁移：从上游任务筛选出高质量的轨迹，将其加入当前任务的elite集合，指导探索。• 任务分解：将复杂目标拆解为子目标，设计多目标奖励函数（如距离、升高、夹爪位置），优化任务特异性表现。• 采样与筛选：在每次迭代中，使用高斯分布采样候选轨迹，依据累计奖励筛选elite轨迹，更新μ和σ。• 结构匹配：利用任务结构匹配指标判断迁移的有效性，确保迁移带来正向贡献。• 实时执行：每个时间步执行最优轨迹的第一个动作，循环进行。• 真实验证：将模拟中学到的轨迹迁移到实际机器人，验证其可行性。整个流程强调知识的快速迁移、结构化优化和奖励引导，提升复杂任务的成功率。
Experiments
- �� 环境设计：在MuJoCo中构建FetchStack、FetchSlide和Shelf任务，模拟多物体堆叠、滑动和货架放置场景。• 基线比较：采用随机采样、CEM、iCEM、TQC+HER、CEM+TL等方法进行对比，评估成功率和样本效率。• 超参数：采样数设为40，精英集大小20，规划时间步长H分别为50或1000。• 迁移策略：从不同上游任务（如单物体堆叠、推拉任务）迁移参数，分析迁移效果。• 消融研究：剔除迁移或奖励重设计模块，评估各组件贡献。• 真实机器人：在Franka Emika机器人上执行模拟最优轨迹，验证迁移策略的实际效果。• 反复试验：每个实验重复三次，统计平均成功率和标准差，确保结果可靠。
Results
- �� 在模拟环境中，FetchStack任务中，iCEM+TL成功率比传统iCEM提升23%，在长远堆叠任务中表现优异。• 迁移自不同上游任务的效果差异显著，结构相似性高的任务迁移效果更佳，验证了结构匹配指标的有效性。• 在真实机器人上，基于模拟最优轨迹的迁移策略成功完成堆叠任务，验证了方法的实用性和鲁棒性。• 消融实验显示，迁移参数和奖励重设计的结合效果优于单一策略，成功率提升明显。• 迁移策略在不同任务和环境中表现出良好的泛化能力，尤其在复杂、多目标场景中优势明显。
Applications
- �� 工业自动化：机器人在装配线上的多物体堆叠、货架管理等任务中，可快速适应新任务，减少调试时间。• 服务机器人：在家庭或商业环境中，机器人可自主学习新任务，如整理、搬运，提升效率。• 远程操作：在危险或难以到达的环境中，利用迁移策略快速部署机器人执行复杂操作。• 未来，结合高层策略与低层规划，将实现更智能的自主操作系统，广泛应用于制造、物流、医疗等领域。
Limitations & Outlook
- �� 迁移效果高度依赖任务结构的相似性，结构差异较大时，迁移参数可能无效甚至带来负面影响。• 在动态或极端复杂环境中，迁移参数的适应性不足，需引入在线调整机制。• 当前验证主要在模拟和单一真实场景，泛化能力仍需在多样化环境中验证。• 计算成本在长时间、多目标任务中较高，需优化算法效率。未来应研究迁移策略的自动选择和环境适应机制，提升鲁棒性和通用性。
Plain Language Accessible to non-experts
想象你在厨房里准备一顿大餐。你之前学会了切菜、炒菜，现在要做一道新菜。虽然新菜看起来很复杂，但你可以把它拆成几个简单的步骤，比如准备食材、调味、炒制。你用之前学到的技巧（比如切菜的方式）来帮助你快速完成新菜，而不用每次都从零开始学。这个过程就像机器人在做复杂任务时，利用之前学到的经验（迁移学习），结合任务拆解（任务分解）和奖励重塑（奖励设计），让机器人更快、更好地完成任务。就像你用已有的厨艺经验快速搞定新菜一样，机器人也能用以前的“经验”来应对新挑战。这种方法节省时间，提高效率，让机器人变得更聪明、更灵活。
ELI14 Explained like you're 14
想象你在玩一款超级复杂的游戏，里面有很多关卡和任务。刚开始，你可能需要花很多时间学习每个关卡的玩法，但如果你之前玩过类似的游戏，或者完成过一些类似的任务，你就可以用之前学到的技巧来快速过关。这就像机器人在学习新任务时，利用以前学到的经验，把它们“搬过来”帮忙。比如，机器人之前学会了把一个物体放到指定位置，现在要堆叠多个物体，它可以用之前的经验作为起点，然后逐步调整，变得更快更准。这就像你用之前的攻略，帮你轻松搞定新关卡一样。通过这种方法，机器人不用每次都从零开始学，而是用以前的“攻略”快速应对新挑战。这让机器人变得更聪明，也更快能完成复杂的任务，就像你在游戏中变得更厉害一样！
Abstract
As robotic systems become more sophisticated, the growing complexity of their motion planning models and the longer training times pose substantial challenges. Evolutionary algorithms such as the Sample-efficient Cross-Entropy Method (iCEM) have recently demonstrated promising potential for low-level real-time planning by leveraging efficient knowledge reuse strategies to improve performance. Although effective in many control tasks, iCEM's performance can be constrained in more complex scenarios, particularly those requiring stacking, sliding, and shelf placement. In this work, we propose a novel iCEM+TL framework that explicitly leverages Transfer Learning (TL), where key iCEM parameters are transferred from simpler upstream tasks to guide more complex downstream tasks. Additionally, we applied Reward Redesign (RR) through task decomposition for stacking objects and shelf placement to optimize task-specific performance. Results from the simulation show that our framework achieves success rate improvements of up to 23%. The framework is further validated on a real Franka Emika robot in a stacking task, demonstrating its practical feasibility for real-world deployment.
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