LIBERO Benchmark: Lifelong Robot Learning

• LIBERO benchmark is a standardized platform for evaluating sequential knowledge transfer in robotic manipulation tasks across diverse settings.
• It employs a modular procedural generation pipeline, comprehensive evaluation protocols, and high-quality human demonstrations to assess both declarative and procedural knowledge.
• Experimental findings reveal that sequential finetuning can outperform standard lifelong learning baselines and highlight the nuanced impact of pretraining strategies.

LIBERO is a standardized benchmark suite designed for lifelong robot learning in sequential decision-making, fundamentally focused on manipulation tasks requiring both declarative and procedural knowledge transfer. By providing a modular procedural generation pipeline for task construction, comprehensive evaluation protocols, and publicly available demonstration datasets, LIBERO enables rigorous investigation of forward and backward transfer, architecture and algorithmic design, curriculum robustness, and the impacts of pretraining across a heterogeneous suite of manipulation challenges.

1. Benchmark Scope and Objectives

LIBERO sets out to address the transfer of knowledge over sequences of robotic manipulation tasks, with particular scrutiny on both declarative knowledge (object concepts, spatial designators) and procedural knowledge (sequential behaviors, manipulation skills). Unlike traditional lifelong learning benchmarks in image/text domains, which predominantly test declarative concept transfer, LIBERO foregrounds the challenge of continuous decision-making in robotics—requiring the integration of rich, multi-modal sensory streams and temporally abstracted control policies.

Its central aim is to provide a systematic testbed for evaluating:

• The efficiency of knowledge transfer between tasks (forward transfer)
• The retention of previously acquired skills after new task acquisition (backward transfer)
• Policy robustness to task sequence ordering
• The effects of pretraining, including how simple supervised pretraining may inhibit adaptability

2. Procedural Task Generation and Suite Design

LIBERO employs an extendible procedural pipeline capable of generating an unbounded set of language-conditioned manipulation tasks. The pipeline includes:

• Behavioral Template Extraction: Uses large video activity datasets (such as Ego4D) to mine human activity templates, which are mapped into natural language commands (e.g., “Open the drawer,” “Place the bowl on the plate”).
• Initial State Specification ($\mu_0$): The simulation samples a detailed scene configuration (object types, positions, orientations, and statuses) encoded in PDDL-like language.
• Goal Definition ($g$): Tasks specify PDDL-style goal predicates, such as unary properties (Open(X)) or binary spatial relations (On(A, B)).

For comprehensive benchmarking, LIBERO provides:

• Three focused suites—LIBERO-Spatial, LIBERO-Object, LIBERO-Goal (10 tasks each)—isolating spatial, object, and goal knowledge respectively
• LIBERO-100 (segregated as LIBERO-90 for training and LIBERO-Long for evaluation), comprising 100 tasks that entangle multiple knowledge types

Each task is paired with high-quality human teleoperated demonstrations to facilitate sample-efficient learning and reproducibility.

3. Key Research Dimensions

LIBERO structures experimentation around five pivotal lifelong learning topics:

1. Transfer of Knowledge Types: Studies span both declarative (e.g., object identity) and procedural (manipulation strategy) knowledge, as well as their hybridization within a single policy framework.
2. Policy Architecture Evaluation: Multi-modal neural networks are benchmarked, covering architectures such as ResNet-RNN (convolutional with recurrent temporal integration), ResNet-T (convolutional with transformer temporal integration), and ViT-T (vision transformer based).
3. Algorithmic Approaches in LLDM: The platform compares canonical lifelong learning algorithms:
   • Memory-based approaches (Experience Replay)
   • Regularization-based (Elastic Weight Consolidation)
   • Dynamic architecture methods (PackNet)
   • Sequential finetuning and multitask learning as reference boundaries
4. Task Ordering Robustness: Evaluates how curriculum sequencing impacts skill transfer and retention, probing for order-induced performance variation.
5. Impact of Pretraining Strategies: LIBERO experiments suggest that naïve supervised pretraining on large-scale offline datasets may reduce downstream LLDM adaptability, highlighting the need for more nuanced pretraining paradigms.

4. Technical Formulation and Evaluation Metrics

The LIBERO lifelong learning scenario is formalized as a finite-horizon Markov Decision Process:

$$\mathcal{M} = \left( \mathcal{S}, \mathcal{A}, \mathcal{T}, H, \mu_0, R \right)$$

with goal-conditional sparse-reward setting:

$g: \mathcal{S} \rightarrow \{0,1\}$

and the objective to maximize:

$$J(\pi) = \mathbb{E}_{s_t,a_t \sim \pi, \mu_0} \left[ \sum_{t=1}^H g(s_t) \right]$$

For lifelong learning across $K$ tasks $T^1,...,T^K$ with a task-conditioned policy:

$\pi(\cdot | s; T)$

the overall objective is:

$$J_{LRL}(\pi) = \frac{1}{K} \sum_{p=1}^K \mathbb{E}_{s_t,a_t \sim \pi(\cdot; T^p), \mu_0^p} \left[ \sum_t g^p(s_t) \right]$$

Behavioral cloning for imitation learning employs:

$$J_{BC}(\pi) = \frac{1}{K} \sum_{p=1}^K \mathbb{E}_{o_t,a_t \sim D^p} \left[ \sum_t \mathcal{L}(\pi(o_{\leq t}; T^p), a_t^p) \right]$$

Performance is quantified using:

• Forward Transfer (FWT)
• Negative Backward Transfer (NBT)
• Area under Success Rate Curve (AUC)

5. Experimental Findings

LIBERO’s curated experiments surface several noteworthy findings:

• Sequential finetuning (SeqL) surpasses established lifelong learning baselines (ER, EWC, PackNet) in forward transfer, challenging assumptions about the efficacy of naïve curriculum transfer given sufficient model capacity.
• No single visual encoder excels universally; transformer-based encoders (ResNet-T, ViT-T) are advantageous when temporal abstraction is needed, whereas convolutional encoders are competitive for procedural skill isolation.
• Pretrained language embeddings as task identifiers (BERT, CLIP, GPT-2) do not robustly enhance performance beyond simple embedding-based task IDs, suggesting sentence embeddings function primarily as bag-of-words discriminators in this setting.
• Naive supervised pretraining may impair downstream lifelong learning, as fixed representations reduce flexibility for adaptation.

6. Resources and Community Infrastructure

LIBERO is distributed as an open community resource, with code, datasets, and experiment guidelines accessible via https://libero-project.github.io. This infrastructure facilitates reproducible research and longitudinal progress tracking in lifelong robot manipulation, while also serving as a reference for integration with complementary systems and datasets.

7. Significance and Role within Lifelong Learning Research

As an ambitious and well-formalized platform, LIBERO is instrumental for disentangling the mechanisms of knowledge transfer in robot learning. Its modular design and broad task coverage render it particularly suitable for exploring generalist agent architectures, curriculum strategies, and the interaction between procedural and declarative skill domains. Findings from LIBERO prompt critical re-evaluation of common practices, such as pretraining regimes and network architecture choices, furthering the discourse in lifelong robotic learning.

The benchmark’s clear mathematical underpinning, robust multi-modal datasets, and systematic evaluation structure mark it as a standard for the assessment and development of lifelong learning algorithms in robotic decision-making.