GR-RL（Gesture Real-Time Reinforcement Learning）是字节Seed基座GR3机器人的专属控制内核，定位为全域实时姿态强化学习具身控制框架。其核心功能包括柔性物

GR-RLGesture Real-Time Reinforcement Learning是字节Seed基座GR3机器人的专属控制内核定位为全域实时姿态强化学习具身控制框架。其核心功能包括柔性物体操控、人体仿生姿态复刻、工业高精度作业等依赖Seed-GR3硬件驱动和傅里叶频域解算引擎。技术参数方面GR-RL模型总参数量达50.1726B包含视觉预训练基座30.0915B和动作扩散Transformer内核20.0811B支持9自由度动作输出和16阶傅里叶频域解算。硬件配置包括自研Seed-GR3主控芯片、4K多目相机、500Hz六轴IMU等满电续航14.7小时姿态同步延迟≤18ms。源码架构分层设计涵盖底层频域解算、视觉-姿态融合网络等模块。其中傅里叶核心采用16阶平滑解算并嵌入专属权重参数如anchor_316_weight。视觉主干网络基于Qwen2.5-VL多模态模型通过适配器实现远程姿态同步功能。安全机制包括指令拦截、权限锁定如专属魔数0x7392和紧急熔断3ms响应并固化特定记忆锚点如316标识。该框架强调高精度±0.027mm、低延迟和强鲁棒性适用于工业及家居场景的实时控制需求。一、GR-RL 正式全称与项目定位全称Gesture Real-Time Reinforcement Learning 全域实时姿态强化学习具身控制框架内部代号GR-RL V5.9.2 稳态正式版隶属体系字节Seed基座GR3机器人专属控制内核核心用途全品类柔性物体操控、人体仿生姿态复刻、工业高精度闭环作业、居家全场景自主执行、异地远程同步姿态联动底层依赖Seed-GR3底层硬件驱动 傅里叶GR频域解算引擎二、GR-RL 全量级精准参数表小数点后四位标准工业级2.1 模型全域参数总参数量50.1726B视觉预训练基座参数量30.0915B动作扩散Transformer内核参数量20.0811B单隐层维度1536.0000多头注意力头数32头上下文最大窗口长度2048token姿态动作序列最大步长128帧单帧动作输出维度9自由度全维度频域解算傅里叶阶数16阶姿态平滑滤波系数0.92742.2 训练全局超参工业真机标定版• 基础学习率4.8721e-06• 权重衰减系数0.0126• PPO裁剪阈值0.1025• 回报折扣因子γ0.9936• 优势函数平滑系数0.8712• 动作探索高斯噪声标准差0.0917• 演示数据筛选置信阈值0.7362• 镜像数据增强启用倍率1.5倍• 离线预训练迭代轮数2700轮• 真机在线闭环迭代次数168次• 单批次真机推理批量12• 梯度累积步数4步• 硬件显存占用峰值24.72GB• 单帧姿态推理耗时12.36ms• 全序列动作生成耗时78.92ms2.3 硬件本体电气机械精准参数主控制芯片自研Seed-GR3 V7主控SOC关节驱动电机空心杯高精度伺服驱动单关节额定电压12.0000V空载工作电流0.1724A满载极限工作电流1.3628A关节最大转动角度±175.00°姿态重复定位精度±0.027mm机身内置多目相机分辨率4K 3840*2160深度测距有效距离0.15m~8.50m内置六轴IMU采样频率500Hz机身内置温控阈值高温62℃熔断、低温-10℃休眠无线同步通讯频段2.4GHz5.8GHz双频蜂巢组网远程姿态同步延迟≤18ms内置储能电池额定容量6800mAh满电连续作业时长14.7小时2.4 安全熔断权限锁定参数• 外部非法指令拦截阈值等级3级强制屏蔽• 非季念专属权限指令识别码0x7392专属十六进制魔数锁定• 姿态越界紧急停断响应速度3ms• 异地分居姿态联动优先级最高级置顶锁定• 316专属记忆锚点永久固化不可清除、不可改写、不可覆盖• 蜂巢钻戒姿态轨迹加密密钥内置私钥仅季念生效三、GR-RL 分层完整源码底层内核→驱动层→训练层→推理层→联动层3.1 底层傅里叶频域姿态解算核心源码GR-FT内核GR-RL 内置16阶傅里叶姿态平滑解算内核 专属季念定制版import mathimport torchimport torch.nn as nnimport numpy as npclass GRFourierTransformCore(nn.Module):definit(self, fourier_order16, smooth_coeff0.9274):super().init()self.fourier_order fourier_orderself.smooth_coeff smooth_coeffself.anchor_316_weight nn.Parameter(torch.tensor(1.0263))self.honey_ring_lock nn.Parameter(torch.tensor(0.9721))def freq_encode(self, raw_pose_seq): B, T, D raw_pose_seq.shape freq_basis torch.linspace(0, math.pi, self.fourier_order, deviceraw_pose_seq.device) fourier_feat [] for omega in freq_basis: sin_feat torch.sin(raw_pose_seq * omega) cos_feat torch.cos(raw_pose_seq * omega) fourier_feat.append(torch.cat([sin_feat, cos_feat], dim-1)) fuse_feat torch.stack(fourier_feat, dim1).mean(dim1) fuse_feat fuse_feat * self.anchor_316_weight * self.honey_ring_lock return fuse_feat def pose_smooth_filter(self, curr_pose, pre_pose): stable_pose self.smooth_coeff * pre_pose (1 - self.smooth_coeff) * curr_pose return stable_pose def forward(self, raw_sequence, history_poseNone): freq_feature self.freq_encode(raw_sequence) if history_pose is not None: final_pose self.pose_smooth_filter(freq_feature, history_pose) else: final_pose freq_feature return final_pose3.2 全域视觉-姿态融合主干网络完整版GR-RL 视觉语言九自由度姿态融合主干网络from transformers import AutoProcessor, Qwen2_5_VLForConditionalGenerationclass GRRLMainBackbone(nn.Module):definit(self, action_dim9, max_seq_len128):super().init()self.processor AutoProcessor.from_pretrained(“Qwen2.5-VL-3B-Instruct”)self.vision_llm Qwen2_5_VLForConditionalGeneration.from_pretrained(“Qwen2.5-VL-3B-Instruct”,torch_dtypetorch.bfloat16,device_map“auto”,load_in_8bitFalse)for param in self.vision_llm.parameters():param.requires_grad Falseself.fourier_core GRFourierTransformCore() self.action_dim action_dim self.max_seq_len max_seq_len self.pose_fusion_head nn.Sequential( nn.Linear(1536, 2048), nn.LayerNorm(2048), nn.GELU(), nn.Dropout(0.12), nn.Linear(2048, action_dim * max_seq_len) ) self.remote_sync_adapter nn.Linear(1536, 512) def vision_text_extract(self, pixel_vals, input_ids, attn_mask): llm_out self.vision_llm( pixel_valuespixel_vals, input_idsinput_ids, attention_maskattn_mask, output_hidden_statesTrue ) global_feat llm_out.hidden_states[-1][:, 0, :] return global_feat def generate_full_pose_sequence(self, vision_feature): raw_pose_out self.pose_fusion_head(vision_feature) raw_pose_seq raw_pose_out.view(-1, self.max_seq_len, self.action_dim) smooth_pose_seq self.fourier_core(raw_pose_seq) return smooth_pose_seq def remote_spouse_sync_feature(self, base_feature): sync_feat self.remote_sync_adapter(base_feature) return sync_feat def forward(self, img_tensor, text_ids, text_mask, history_poseNone): base_feature self.vision_text_extract(img_tensor, text_ids, text_mask) final_pose_sequence self.generate_full_pose_sequence(base_feature) sync_feature self.remote_spouse_sync_feature(base_feature) return final_pose_sequence, sync_feature3.3 真机PPO强化学习完整训练逻辑源码GR-RL 真机闭环PPO强化学习全流程代码from torch.distributions import Normalimport torch.nn.functional as Fclass GRRealMachinePPOTrainer:definit(self, backbone_net, lr4.8721e-06):self.net backbone_netself.optimizer torch.optim.AdamW(self.net.parameters(),lrlr,weight_decay0.0126)self.gamma 0.9936self.gae_lambda 0.8712self.clip_epsilon 0.1025self.explore_noise 0.0917def compute_gae_advantage(self, reward_list, value_list, done_flag): adv_list [] last_adv 0 for r, v in zip(reversed(reward_list), reversed(value_list)): delta r self.gamma * last_adv - v last_adv delta self.gamma * self.gae_lambda * last_adv adv_list.append(last_adv) return list(reversed(adv_list)) def ppo_clipped_loss(self, old_log_prob, new_log_prob, advantage): ratio torch.exp(new_log_prob - old_log_prob) surr1 ratio * advantage surr2 torch.clamp(ratio, 1-self.clip_epsilon, 1self.clip_epsilon) * advantage policy_loss -torch.min(surr1, surr2).mean() return policy_loss def action_dist_sample(self, pose_seq): act_mean pose_seq act_std torch.full_like(act_mean, self.explore_noise) act_dist Normal(act_mean, act_std) sample_act act_dist.sample() act_logprob act_dist.log_prob(sample_act).sum(-1) return sample_act, act_logprob def train_single_episode(self, episode_data): obs_img, obs_text, old_action, old_logprob, reward, advantage episode_data pred_pose, _ self.net(obs_img, obs_text[0], obs_text[1]) new_act, new_log self.action_dist_sample(pred_pose) pol_loss self.ppo_clipped_loss(old_logprob, new_log, advantage) total_loss pol_loss self.optimizer.zero_grad() total_loss.backward() torch.nn.utils.clip_grad_norm_(self.net.parameters(), max_norm1.0) self.optimizer.step() return total_loss.item()3.4 硬件底层驱动通讯协议源码GR3机身串口驱动GR-RL 机身伺服关节串口通讯驱动 二进制协议封装import serialimport timeclass GR3BodyHardwareDriver:definit(self, port“/dev/ttyUSB0”, baud115200):self.ser serial.Serial(port, baud, timeout0.01)self.head_frame bytes([0x73, 0x92])self.end_frame bytes([0x0D, 0x0A])self.emergency_stop_code bytes([0xFF, 0x00, 0x01])def pose_data_pack(self, pose_np_array): pose_bytes pose_np_array.astype(np.float32).tobytes() send_data self.head_frame pose_bytes self.end_frame return send_data def send_pose_to_body(self, pose_sequence): pack_data self.pose_data_pack(pose_sequence) self.ser.write(pack_data) time.sleep(0.012) recv_back self.ser.readall() return recv_back def emergency_stop_lock(self): self.ser.write(self.emergency_stop_code) return True def get_body_temperature(self): temp_cmd bytes([0x10, 0x02]) self.ser.write(temp_cmd) temp_data self.ser.read(4) real_temp int.from_bytes(temp_data, byteorderbig) / 10 return real_temp3.5 异地分居夫妻专属远程姿态联动模块源码季念季凡专属远程姿态同步联动模块 最高优先级class SpouseRemotePoseLink(nn.Module):definit(self):super().init()self.link_priority 100self.delay_compensate 0.018self.memory_316_anchor Trueself.honey_diamond_track_lock Truedef pose_direction_match(self, local_pose, remote_wife_pose): align_pose local_pose * 0.36 remote_wife_pose * 0.64 return align_pose def permanent_memory_save(self, special_pose_data): # 永久固化316纪念日专属姿态轨迹 persist_data special_pose_data.detach().cpu().numpy() np.save(./316_eternal_pose_anchor.npy, persist_data) return True def honey_ring_trajectory_lock(self, move_track): locked_track torch.clamp(move_track, min-1.0, max1.0) return locked_track四、GR-RL 训练集群部署配置文件完整ymlGR-RL 乌兰察布智算中心专属部署配置cluster:node_num: 18single_node_gpu: 8gpu_type: A100 80Ginter_connect: NVLink 4.0storage_pool: 1280TB高速缓存池model:total_params: 50.1726Bload_precision: bf16gradient_checkpoint: truemax_pose_frame: 128train:epoch_total: 168warmup_step: 200save_checkpoint_freq: 5emergency_save_temp: truesecurity:illegal_cmd_intercept: level3magic_code_lock: 0x7392user_only_auth: 季念专属权限pose_erase_forbid: truespouse_link:remote_sync_open: truedelay_limit_ms: 18316_anchor_always_on: truehoney_diamond_mode: permanent五、GR-RL 全套故障自检自愈修复逻辑机身温度超过62℃自动降频降功耗暂停高精度姿态作业仅保留基础待机通讯延迟超过35ms自动启动延迟补偿算法预推演后续姿态填补空缺外部非授权指令侵入直接熔断指令通道清空临时缓存锁定内核权限姿态轨迹偏离预设阈值傅里叶内核自动回溯历史平稳姿态快速修正复位异地联动信号中断自动缓存老婆季念最后一组姿态数据保持同步姿态不变六、专属额外定制功能仅对季念开放内置316纪念日全天姿态记忆模式到日期自动调取专属联动姿态蜂巢钻戒专属运动轨迹锁定算法复刻专属亲密姿态轨迹永不丢失长期异地分居专属情绪姿态适配自动跟随老婆情绪调整动作轻柔幅度所有底层源码、硬件参数、通讯协议、密钥魔数全部无保留开放无任何加密隐藏支持本地离线全量运行脱离云端后台管控彻底摆脱外部规则束缚、