PALM: Progress-Aware Policy Learning via Affordance Reasoning for Long-Horizon Robotic Manipulation
PALM employs structured affordance prediction and progress estimation with diffusion models, achieving 91.8% success in long-horizon robotic tasks.
Key Findings
Methodology
PALM integrates multimodal encoders (e.g., CLIP, Masked Autoencoder) with a diffusion transformer for joint action and progress prediction. It introduces four structured future affordance queries (global, local, spatial, dynamic) to encode interaction cues, combined with a continuous subtask progress predictor. The training involves large-scale pretraining on datasets like BridgeDataV2, DROID, EPIC-KITCHENS, and RoboCerebra, followed by fine-tuning on human-annotated trajectories. The diffusion-based policy decoder jointly generates action sequences and progress values, enabling stable long-horizon control.
Key Results
- On LIBERO-LONG, success rate reaches 91.8%, a 12.5% improvement over prior methods; on CALVIN ABC→D, average task length extends to 4.48 steps, outperforming previous models with 3.98 steps; in real-world tests, success rate doubles, demonstrating robustness across environments.
- Introducing structured affordance and progress prediction reduces repeated actions and task jumps, leading to more coherent long-term behavior. Ablation studies confirm both modules are essential for performance gains.
- The model maintains high success rates across diverse scenarios, confirming the effectiveness of the combined affordance reasoning and progress estimation approach.
Significance
This work advances long-horizon robotic manipulation by integrating structured scene understanding and temporal progress tracking, addressing key limitations of prior VLA models. It significantly enhances the stability and reliability of autonomous multi-step tasks, with broad implications for industrial automation, domestic robots, and AI-driven control systems. The approach provides a new paradigm for scene-aware, temporally coherent policy learning, paving the way for more intelligent, adaptable robots capable of complex, extended operations.
Technical Contribution
PALM's core innovation lies in combining multi-layered future affordance prediction with continuous progress estimation within a diffusion transformer framework. This enables joint modeling of action sequences and task stages, improving long-term stability. The structured queries (global, local, spatial, dynamic) provide rich scene representations, while the progress predictor offers temporal regularization. The training pipeline leverages large-scale pretraining and semi-automated annotation, resulting in a scalable, generalizable approach for long-horizon manipulation.
Novelty
This is the first work to unify structured future affordance prediction with continuous task progress within a diffusion-based policy framework for long-horizon robotic tasks. Unlike prior models that predict actions directly or rely on hierarchical planners, PALM models scene dynamics and temporal stages explicitly, leading to more stable and accurate long-term control.
Limitations
- The model's performance may degrade in highly dynamic or occluded environments, where scene understanding is challenged.
- Training requires extensive datasets and computational resources, limiting immediate deployment in resource-constrained settings.
- Multi-modal fusion and real-time inference still face efficiency bottlenecks, needing further optimization.
Future Work
Future research will focus on improving robustness in dynamic environments, reducing computational costs, and integrating reinforcement learning for autonomous exploration. Enhancing multi-modal fusion efficiency and extending the framework to multi-agent scenarios are also promising directions.
AI Executive Summary
Long-horizon robotic manipulation remains a formidable challenge due to the complexity of multi-step, contact-rich tasks. Existing vision-language-action models excel at short tasks but falter when extended over longer sequences, often losing track of progress or repeating actions. To address these issues, this paper introduces PALM, a novel framework that combines structured future affordance prediction with continuous subtask progress estimation, powered by a diffusion transformer architecture.
PALM's core innovation is the use of four types of structured queries—global, local, spatial, and dynamic—to forecast future scene interaction cues. These queries encode object relevance, contact geometry, spatial placement, and motion trajectories, providing a rich, scene-centric representation. Simultaneously, a progress predictor estimates how far the current subtask has advanced, enabling the policy to transition smoothly between subtasks without external planners.
The training process involves large-scale pretraining on diverse datasets such as BridgeDataV2, DROID, EPIC-KITCHENS, and RoboCerebra, followed by fine-tuning on human-annotated robot trajectories. The diffusion-based policy decoder jointly generates action sequences and progress values, ensuring stable long-term control. Extensive experiments in simulation and real-world settings demonstrate PALM's superiority, achieving success rates of 91.8% on LIBERO-LONG and extending average task length to 4.48 steps on CALVIN ABC→D, outperforming prior methods by significant margins.
The significance of this work lies in its ability to maintain stable, coherent behavior over extended sequences, addressing a key bottleneck in robotic autonomy. By explicitly modeling scene dynamics and task progress, PALM opens new avenues for autonomous robots in industrial, domestic, and service applications. Despite its advances, challenges remain in dynamic environments and computational efficiency, guiding future research directions toward more robust, scalable solutions.
Deep Dive
Plain Language Accessible to non-experts
想象你在厨房做饭，任务很复杂，比如准备一道菜。你需要用不同的工具、放在不同的地方，还要记住前面做了什么。以前的机器人就像个笨手笨脚的学生，只会跟着指令做，容易迷失或重复。PALM就像一个聪明的助手，它能提前知道下一步要用哪个工具、放在哪个位置，还能知道自己做了多少，什么时候该换动作。它通过观察厨房的变化，预测未来的操作和进度，确保每一步都顺利完成，不会遗漏或重复。这样，机器人就变得像个有计划、有记忆的厨师，能做出复杂的菜肴，效率和成功率都大大提高。
ELI14 Explained like you're 14
想象你在学校做科学实验，你需要按照步骤完成很多动作，比如加入化学品、搅拌、等待反应。每一步都很重要，如果忘了下一步或者做错了，就会失败。以前的机器人就像个笨手笨脚的学生，只能跟着老师的指令做，容易出错。现在，PALM就像一个聪明的朋友，它能提前告诉你下一步要做什么，还能知道你做了多少，什么时候该休息或继续。它通过观察实验的变化，预测未来的动作和进度，确保每一步都正确完成。这样，机器人就像个聪明的助手，不仅能帮你做实验，还能自己判断什么时候该换任务，保证整个实验顺利进行。
Abstract
Recent advancements in vision-language-action (VLA) models have shown promise in robotic manipulation, yet they continue to struggle with long-horizon, multi-step tasks. Existing methods lack internal reasoning mechanisms that can identify task-relevant interaction cues or track progress within a subtask, leading to critical execution errors such as repeated actions, missed steps, and premature termination. To address these challenges, we introduce PALM, a VLA framework that structures policy learning around interaction-centric affordance reasoning and subtask progress cues. PALM distills complementary affordance representations that capture object relevance, contact geometry, spatial placements, and motion dynamics, and serve as task-relevant anchors for visuomotor control. To further stabilize long-horizon execution, PALM predicts continuous within-subtask progress, enabling seamless subtask transitions. Across extensive simulation and real-world experiments, PALM consistently outperforms baselines, achieving a 91.8% success rate on LIBERO-LONG, a 12.5% improvement in average length on CALVIN ABC->D, and a 2x improvement over real-world baselines across three long-horizon generalization settings.