Agent Priors-guided Policy Learning

0:00Welcome to Daily Papercast. Today's paper is from the Hugging Face Daily Paper List of October 2nd, 2026, with 44 upvotes. The paper is titled, Agent Pryors, Guided Policy Learning. The first two authors are Puming Jiang and Tianran Hu, and the corresponding author is Harold Soe. They are from the National University of Singapore. Let's dive into the introduction. The paper discusses how robots that learn from a few demonstrations often require two types of generalization, compositional and skill generalization. Compositional generalization involves recombining skills to solve new tasks, while skill generalization lets the learned policy work in new situations. These types of generalizations are interdependent. However, information is often lost between the task level and the skill level. This is because the task level deals with goals and sequences, whereas the skill level deals

1:01with specific policies trained from a few demonstrations. Existing approaches have tried to connect the task and skill levels in different ways. For example, vision language action models couple task semantics and low level control in a single model, but still face limitations in robustness when layouts and objects shift. Another example is task and motion planning, which connects them through symbolic preconditions and effects. These are either specified by hand or learned from data, but transitions between skills can still fail. Agentex systems can call policies as tools and use language as the interface, yet they often struggle to ground their decisions in the tools' actual ability, leading to failures during transitions. The key idea of this paper is to use each skill's structural prior as part of the interface between composition and the skill. A structural prior specifies how a skill is designed to generalize. This can be implemented in the skill through its representation or training objective, and

2:01it shapes how the skill generalizes beyond its demonstrations. For instance, a grasp skill might be trained in an object relative frame, which then informs where the policy should be applicable. During deployment, the runtime agent reads this prior and uses it to decide which policy to invoke based on the current task requirements. The paper proposes a method called AgentPriorsGuidedPolicyLearning or APL. At its core, APL uses structural priors that help shape the trained policies and provide runtime agent information about when those policies should be applicable. To implement APL, a construction agent segments complete demonstrations into reusable skills, proposes several structural priors for each skill, and trains one documented policy per prior. These policies are then verified from demonstrated entry states and stored in a frozen skill library. At runtime, a separate agent reads these interfaces to choose among the frozen policies,

3:02instantiate their arguments and stopping conditions, and compose them toward new task goals. This way, information that shaped a policy during learning remains available when that policy is later selected and composed. The effectiveness of APL is demonstrated across a range of tasks from meta-world and long horizon manny skill tasks. Results show improvements in out-of-distribution skill generalization and enable previously unseen skill compositions. Furthermore, the paper discusses a blading interface information, which substantially reduces performance. These results support the use of training time structural assumptions as a bridge between skill learning and skill composition. And that's the end of the introduction section of the paper. Alright, let's dive into the methodology section of the paper. The authors introduce agent priors guided policy learning, or APL, which fundamentally changes how policies are learned and selected during runtime.

4:03To begin with, APL relies on two main agents, the construction agent and the runtime agent. The construction agent operates offline and has a series of tasks including segmenting demonstrations, proposing priors, training policies, and verifying them. Right, the construction agent starts by segmenting complete demonstrations into reusable skills. It examines each trajectory and finds distinct physical responsibilities within it. It intentionally overlaps adjacent skill segments around their transitions or handoffs. This overlapping includes part of the end of its predecessor and the beginning of its successor, broadening the training support around these handoff states. Which helps, because the successor policy can now take over from states that the predecessor skill actually reaches, including states before nominal completion. It's important to note that this overlap reuses transitions from the original demonstrations and introduces no additional demonstration data.

5:04After segmenting demonstrations, the construction agent proposes several structural priors for each skill. The idea is not to commit to one predefined abstraction or policy implementation, but rather to have a variety. Each prior encodes assumptions about relations the skill should depend on and how its behavior should respond to different changes in the scene. For example, an object relative prior expresses the assumption that a manipulation behavior can be reused at different absolute object locations when the relevant object relative geometry is preserved. This helps the skill policy generalize beyond its demonstrations. Each proposed prior has two realizations, the training realization and the description realization. The training realization shapes the learned policy. This could involve altering the policy representation, action parameterization, architecture, or introducing auxiliary training objectives. For instance, a policy class they three defined by a specific structural prior might be trained

6:06through the following loss function. The classic behavior cloning loss plus a regularization term derived from the prior itself. The prior shapes what the policy learns and consequently its success region. But how does the runtime agent utilize these priors? Good question. The same prior also forms part of the policy's runtime description. This description communicates the hypothesized applicability region of the policy based on structural assumptions and observed training support. The runtime agent uses this information to select the appropriate policies during task execution. Thus, the prior helps improve the interface faithfulness since it is based on assumptions that shaped the policy. The construction agent also systematically verifies the trained policies before freezing them. Verification involves testing the policy on demonstrated entry states, executing each conditional diffusion policy for a set duration, and evaluating it against the demonstrated exit conditions. What's crucial here is that the verification step uses limited execution evidence coming

7:10only from the demonstrated skill entry states. The agent then writes a verification report separating observed outcomes from its inferences about the policy. After verification, the construction agent records an interface for each trained skill policy, comprising four fields, the prior description, handoff description, support description, and verification evidence. These descriptions give the runtime agent the information it needs about the policy's applicability and training support. Additionally, each interface declares the typed arguments required by the policy. Once the library is constructed and frozen, the runtime agent takes over. The runtime agent receives the task goal, the current state, recent interaction history, and the available policy interfaces. Using this information, it chooses a policy, specifies the arguments, sets an execution duration, and defines stop conditions. If I understand correctly, during task execution, the executor runs the selected policy with

8:12fresh observations, checking the stop conditions after every step. When a stop condition is met, control returns to the runtime agent, which informs the next steps, based on the updated state and goal predicates. Exactly. This enables the runtime agent to continue the current skill, switch policies for the same skill, invoke a different skill, or terminate the task. Importantly, it can choose among alternative implementations for the same skill, based on prior information. Therefore, leveraging the skill libraries learned coverage in new tasks. This mechanism is particularly effective for handling new compositions and object shifts beyond the demonstrations. APL demonstrated superior performance on MetaWorld and many skill tasks compared to conventional full task policies, providing robust skill generalization. Right, and the authors also conducted experiments to test both roles of structural priors. One experiment evaluated the prior designed policies on out-of-distribution states, showing

9:13substantial improvements. Another experiment tested the runtime agent's ability to compose these policies in long horizon tasks under shifted objects and task variants, again showing effectiveness. The results indicate that using structural assumptions, both for learning and as runtime information, significantly boosts performance, and hiding some structural information at runtime substantially reduces success. That's the end of the method section. Up next, we'll discuss the results section of the paper. All right, let's move on to the experiments and results section, which is quite comprehensive in the paper. The authors conducted two primary experiments to evaluate the effectiveness of agent priors guided policy learning or APL. The first experiment focused on skill generalization, while the second examined the composition of those skills in long horizon tasks. Let's start with the first experiment. What did they aim to investigate here?

10:15Experiment one aimed to evaluate the training realization of priors. Specifically, it asked whether an agent can design and implement priors that make a skills policy generalize from a few demonstrations. The tasks were adapted versions of six-meta-world tasks, including pick place wall, assembly, and drawer open, among others. And how are these tasks set up for testing? Each task had twenty successful demonstrations. The test states varied two spatial factors per task into three categories. IID states stayed within the demonstrated factor ranges, C recombined the factors within scene ranges, and E extrapolated both factors beyond their demonstrated intervals, hence out of distribution or OOD. What's intriguing is that some states were completely new combinations not seen during training. Interesting setup. So, what were the findings? The experiment tested six different systems for each demonstration scenario, including a baseline diffusion policy and one with a fixed relational prior.

11:17The results showed that agent design priors significantly improved out of distribution skill generalization across all tasks. Could you give us some specifics? For instance, with only two demonstrations, an agent's best proposal achieved 89.6% UD success, compared to just 28.96% for the vanilla diffusion policy, and 37.92% for the fixed relational prior. Even with increased demonstrations, the trend remained the same, showcasing the significant impact of these agent-designed priors. That's impressive. So what about the second experiment? The second experiment evaluated whether the runtime agent could effectively compose these prior specific skill policies to complete tasks from new states and goals. They used five long horizon mani skill tasks like drawer exchange, buffer exchange, and retrieve and store. Each task had 12 successful demonstrations. What were the methods compared in this part of the experiment?

12:19They compared APL with several baselines, a full task diffusion policy, a single prior full task policy, and a vision language action model orchestrated by the same runtime agent. They also tested three ablated versions of APL that hid certain interface information. And what did they find? Apple outperformed all baselines. For example, on motion level UD tasks, Apple achieved 50.0% success versus 10.0% for both the full task diffusion policy and the single prior full task policy. This indicates that Apple has method of using various skill policies with runtime agent selection is quite effective for out of distribution generalization. Interesting. Did the results include any insights into handling the complexity of long horizon tasks? Yes, absolutely. APL successfully completed various task-level UD and composition cases. It managed to achieve its tasks despite variations like differing starting conditions and intermediate

13:23sub-goals. When the prior related information was hidden, performance dropped significantly. For instance, success and task-level UD cases dropped from 92.5% to 65.0% when prior information was hidden. In essence, the results clearly show that structural assumptions used both in training and as part of the runtime interface greatly improve performance. The experiments highlight that APL can effectively bridge the gap between skill learning and composition. Yes, the experiments also underscore the importance of having detailed structural information accessible to the runtime agent. By making this information available, APL was able to more effectively generalize skills and handle new task variations. And that sums up the experiment and result section of the paper. Let's now explore how this work fits within the broader landscape of research in this field. The authors categorize the related work into a few key areas, emphasizing how their approach

14:26addresses some of the limitations of existing methods. To start, they discuss structural priors in the context of skill generalization. Structural priors, such as object relative frames, are effective in making learned behaviors adaptable to new situations. However, most existing works rely on priors that are fixed or designed manually for specific tasks. Yes, and the authors reference studies like those by Eisner et al in 2022 and Behedi et al in 2024, which focus on specific operations like doors and drawers or screw motions. These studies show that while these priors are effective, their applicability is limited to the specific tasks they were designed for. Interestingly, the paper also points out that even within a task, the right structural prior can vary significantly. For instance, benefits from priors like wrist camera views or 3D point clouds are highly task dependent, as shown in studies by Schu et al in 2022 and Ling et al in 2023.

15:30Moreover, the assumptions behind these priors can limit the achievable accuracy. For example, an incorrect symmetry assumption might hinder performance as discussed by Wang et al in 2023. That's where APL's flexibility becomes a significant advantage. By proposing multiple priors for each skill and letting the runtime agent choose among them, it addresses this limitation. Next, the authors talk about agents and robot tools. They note that interfaces for these tools often include geometric feasibility checks and predicate invention for learned skills, as observed in studies like those by Ling et al in 2023 and Yang et al in 2025. These checks are essential, but don't always capture the complete context required for effective skill execution. APL enhances this by including hand-off overlaps and verification reports, which provide richer context. They also mention symbolic abstractions and skill discovery as another critical area.

16:33Several methods like options and sampler-based planning provide foundational concepts for temporal abstraction and task and motion planning. Right. For example, methods proposed by Konaderis et al in 2018 and Garrett et al in 2020 involve learning symbolic representations from skills, which can be quite powerful. Exactly. However, the APL approach extends these ideas by employing structural priors that inform both policy learning and runtime decision making, a dual role that most traditional methods don't cover. In the same vein, the authors draw contrasts with systems that sequence learned or human guided skills using planners or language models. Studies like those by Mandelkar et al in 2023 and Delal et al in 2024 illustrate these approaches. Yes, and importantly, transition policies and skill-chaining methods aim to manage hand-offs between skills effectively. APL tackles this by using overlapping training segments, which broaden the initiation set

17:37for the successor skill, making transitions smoother. Interesting. So, APL essentially integrates ideas from various existing methods and builds upon them to create a more robust framework for skill generalization and composition. Precisely. The authors also refer to agent-driven systems where models help design rewards, simulation tasks, and even sim-to-real rewards, like in studies by Shi et al in 2024 and Wang et al in 2024. APL leverages similar concepts, but in a more structured and targeted way. That's a lot of integration. How do they differentiate APL from systems using pre-existing policies or coding agents? Great point. The key difference is that APLE derives its documentation from the construction time prior and hand-off overlaps. This is before any actual tasks are executed, unlike systems that update their policy cards or skill memories based on execution. It sounds like APL has a proactive approach rather than a reactive one.

18:41Exactly. By structurally embedding knowledge at both the learning and decision-making stages, APL ensures a more seamless integration of skills in varied and dynamic settings. And that wraps up the related work section of the paper. All right, Ashley. Let's summarize the key contributions and takeaways of this paper. First and foremost, the paper introduces agent-priors guided policy learning or APL. This approach significantly enhances both skill generalization and compositional generalization by embedding structural priors into the learning and decision-making framework. The idea of using structural priors for both training and runtime interface is quite revolutionary. It addresses the common issue where information is lost between task-level goals and skill-level executions. Exactly. By having a construction agent propose and implement multiple priors per skill, APLE creates a versatile skill library.

19:42The runtime agent can then make informed decisions on which policy to apply based on the specific task requirements and current states. Right. And the overlap in training segments allows for smoother skill transitions, making the system more robust in handling varied and out-of-distribution conditions. The experimental results are compelling as well. APL out-performed conventional approaches in both meta-world and long horizon manny skill tasks, especially in out-of-distribution scenarios and new task compositions. The experiments clearly demonstrated that hiding interface information led to substantial performance drops, further validating the importance of accessible structural information for runtime agents. Definitely. Overall, APL shows how integrating structural assumptions as both a learning guide and a runtime interface can vastly improve robot learning and task execution. And that brings us to the end of today's episode on Daily Papercast.

20:43We hope you found this discussion on agent priors guided policy learning insightful. Yes, thank you for joining us. If you enjoyed this episode, be sure to tune in for our next one where we will delve into more cutting-edge research in AI and robotics. Don't forget to subscribe and leave a review if you like the podcast. Until next time, stay curious and keep exploring the frontiers of AI research.