Probabilistic Recurrent Intention Switching Model

• URL: http://arxiv.org/abs/2605.26998v1
• Date: Tue, 26 May 2026 13:19:00 GMT
• Title: Probabilistic Recurrent Intention Switching Model
• Abstract summary: Inverse reinforcement learning (IRL) recovers reward functions from observed behavior, yet traditional methods assume a single stationary reward that cannot capture goal switching within an episode.<n>Recent multi-intention IRL methods address this by segmenting trajectories, but model intention transitions as either a memoryless Markov chain or via manual state augmentation with a fixed history window.<n>We propose the Probabilistic Recurrent Intention Switching Model (PRISM), which replaces both mechanisms with a lightweight recurrent network that maps observation history to a per-step intention distribution.
• Score: 8.25071593831945
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Inverse reinforcement learning (IRL) recovers reward functions from observed behavior, yet traditional methods assume a single stationary reward that cannot capture goal switching within an episode. Recent multi-intention IRL methods address this by segmenting trajectories, but model intention transitions as either a memoryless Markov chain or via manual state augmentation with a fixed history window. We propose the Probabilistic Recurrent Intention Switching Model (PRISM), which replaces both mechanisms with a lightweight recurrent network that maps observation history to a per-step intention distribution. We prove that the resulting EM objective decomposes exactly into independent per-intention reward subproblems, each solvable in closed form, yielding an $\mathcal{O}(nK)$ E-step with no variational approximation. We evaluate PRISM on a non-Markovian gridworld, a mouse labyrinth, and BridgeData~V2 robotic manipulation, the first large-scale robotic application of multi-intention IRL. Across all settings PRISM achieves the highest held-out log-likelihood while recovering nameable, temporally coherent intentions from unlabeled demonstrations, suggesting that discrete goal switching is present in both biological and artificial agents.

Related papers

• SC3-Eval: Evaluating Robot Foundation Models via Self-Consistent Video Generation [73.2142090645987]
  SC3-Eval is a self-consistent video generation recipe that adapts a pre-trained video foundation model into an accurate policy evaluator.<n>We demonstrate SC3-Eval rollouts reproduce the failure modes that policies exhibit in real-world rollouts, supporting fine-grained diagnostic comparison.
  arXiv  Detail & Related papers  (2026-06-17T02:15:46Z)
• Action-Inspired Generative Models [0.0]
  Action-Inspired Generative Models (AGMs)<n>We introduce a dual-network generative framework motivated by the observation that existing bridge-matching methods assign uniform regression weight to every transition in the transport landscape.<n>We demonstrate that selectively penalising uninformative transport paths through the learned potential yields consistent improvements in generation quality across fidelity and coverage metrics.
  arXiv  Detail & Related papers  (2026-05-14T09:43:32Z)
• World Action Verifier: Self-Improving World Models via Forward-Inverse Asymmetry [82.93104394404781]
  General-purpose world models promise scalable policy evaluation, optimization, and planning.<n>We propose World Action Verifier (WAV), a framework that enables world models to identify their own prediction errors and self-improve.
  arXiv  Detail & Related papers  (2026-04-02T12:48:36Z)
• How Far Can Unsupervised RLVR Scale LLM Training? [57.44753418846446]
  Unsupervised reinforcement learning with verifiable rewards (URLVR) offers a pathway to scale LLM training beyond the supervision bottleneck.<n>Recent works leverage model intrinsic signals, showing promising early gains, yet their potential and limitations remain unclear.<n>We classify URLVR methods into intrinsic versus external based on reward sources, then establish a unified theoretical framework.
  arXiv  Detail & Related papers  (2026-03-09T17:38:11Z)
• Primary-Fine Decoupling for Action Generation in Robotic Imitation [91.2899765310853]
  Multi-modal distribution in robotic manipulation action sequences poses critical challenges for imitation learning.<n>We propose Primary-Fine Decoupling for Action Generation (PF-DAG), a two-stage framework that decouples coarse action consistency from fine-grained variations.<n>PF-DAG outperforms state-of-the-art baselines across 56 tasks from Adroit, DexArt, and MetaWorld benchmarks.
  arXiv  Detail & Related papers  (2026-02-25T08:36:45Z)
• TABES: Trajectory-Aware Backward-on-Entropy Steering for Masked Diffusion Models [35.327100592206115]
  Backward-on-Entropy (BoE) Steering is a gradient-guided inference framework that approximates infinite-horizon context via a single backward pass.<n>To ensure scalability, we introduce ttexttActiveQueryAttention, a sparse adjoint primitive that exploits the structure of the masking objective to reduce backward pass complexity.
  arXiv  Detail & Related papers  (2026-01-30T19:10:32Z)
• On the Feasibility of Hijacking MLLMs' Decision Chain via One Perturbation [22.536817707658816]
  A single perturbation can hijack the whole decision chain.<n>Semantic-Aware Universal Perturbations (SAUPs) induce varied outcomes based on the semantics of the inputs.<n>Experiments on three multimodal large language models demonstrate their vulnerability.
  arXiv  Detail & Related papers  (2025-11-25T07:13:13Z)
• Sculpting Latent Spaces With MMD: Disentanglement With Programmable Priors [30.182736043604304]
  We introduce the Programmable Prior Framework, a method built on the Maximum Mean Discrepancy (MMD)<n>Our work provides a foundational tool for representation engineering, opening new avenues for model identifiability and causal reasoning.
  arXiv  Detail & Related papers  (2025-10-13T21:26:01Z)
• Model Checking for Closed-Loop Robot Reactive Planning [0.0]
  We show how model checking can be used to create multistep plans for a differential drive wheeled robot so that it can avoid immediate danger. Using a small, purpose built model checking algorithm in situ we generate plans in real-time in a way that reflects the egocentric reactive response of simple biological agents.
  arXiv  Detail & Related papers  (2023-11-16T11:02:29Z)
• Generative Fractional Diffusion Models [53.36835573822926]
  We introduce the first continuous-time score-based generative model that leverages fractional diffusion processes for its underlying dynamics. Our evaluations on real image datasets demonstrate that GFDM achieves greater pixel-wise diversity and enhanced image quality, as indicated by a lower FID.
  arXiv  Detail & Related papers  (2023-10-26T17:53:24Z)
• Just One Byte (per gradient): A Note on Low-Bandwidth Decentralized Language Model Finetuning Using Shared Randomness [86.61582747039053]
  Language model training in distributed settings is limited by the communication cost of exchanges. We extend recent work using shared randomness to perform distributed fine-tuning with low bandwidth.
  arXiv  Detail & Related papers  (2023-06-16T17:59:51Z)
• Value function estimation using conditional diffusion models for control [62.27184818047923]
  We propose a simple algorithm called Diffused Value Function (DVF) It learns a joint multi-step model of the environment-robot interaction dynamics using a diffusion model. We show how DVF can be used to efficiently capture the state visitation measure for multiple controllers.
  arXiv  Detail & Related papers  (2023-06-09T18:40:55Z)
• Disentangled Sequence Clustering for Human Intention Inference [40.46123013107865]
  Disentangled Sequence Clustering Variational Autoencoder (DiSCVAE) Disentangled Sequence Clustering Variational Autoencoder (DiSCVAE)
  arXiv  Detail & Related papers  (2021-01-23T13:39:34Z)

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.